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This paper presents a surrogate-based Bayesian approach for updating the ground parameters within an application of the observational method in sequential excavation method (SEM) construction. A three-dimensional (3D) finite-difference model is used in the forward analysis to simulate SEM construction explicitly considering 3D multi-face excavation effects and ground–structure interaction. The polynomial-chaos Kriging (PCK) method was employed to provide a surrogate for the 3D finite-difference model to alleviate the cost of probabilistic analysis. The uncertain geotechnical parameters are updated during SEM construction through a progressive Bayesian updating procedure. Time-series observations of multiple types of measurements are used to form the likelihood function. The posterior distributions of the uncertain parameters are derived from the affine invariant ensemble sampling (AIES) algorithm. The proposed framework is illustrated through application to data from the Regional Connector Transit Corridor (RCTC) crossover cavern project constructed in downtown Los Angeles. The uncertainties of the geotechnical parameters were substantially reduced. The posterior estimations indicate higher elastic modulus and cohesion of the Fernando formation than what was assumed before the construction. The updated predictions of the ground surface, subsurface and structural deformations showed improvement in agreement with the field measurements through the continuous updating process. [ABSTRACT FROM AUTHOR]

It is well established that environmental signals can modify the expression of traits during development and induce phenotypic shifts across multiple generations. Theory predicts that the induction of such 'within-generation' and 'transgenerational' plasticity is dependent upon the integration of information provided by genes, parents, and the environment. Here we tested theoretical predictions for the expression of plasticity using resurrected populations of waterfleas (Daphnia pulicaria) from lakes in Wisconsin, USA (mostly) prior to the proliferation of a novel predator (spiny waterflea, Bythotrephes longimanus). We reared Daphnia in the presence and absence of Bythotrephes cues for five generations and assessed within- and transgenerational plasticity (anti-predator behaviour) in multiple time periods. Our results show that initial exposure to Bythotrephes cues induced strong behavioural responses in Daphnia ; however, parental and offspring reaction norms were in opposite directions. We then show that continued exposure to predator cues and, in turn, increased reliability of information provided to parental Daphnia facilitated a quick alignment between parent and offspring reaction norms and the erosion of plasticity over time. Our results provide new insights into the factors that influence the induction, strength, and direction of transgenerational responses to environmental signals. These results contribute to a broader understanding of non-genetic inheritance and its evolutionary implications in coping with environmental changes. [ABSTRACT FROM AUTHOR]

Variance global sensitivity (VGS) is defined by the mean square difference between output expectation and conditional one on input realization, and it can calculate the mean contribution of the input within its distribution region and guide the effective modulation of output variance. The Monte Carlo simulation (MCS) and quasi MCS are commonly used to estimate VGS, but they are time-consuming respectively due to double-loop framework and computation related to input dimension. Thus, a novel method is proposed to estimate VGS by elaborately using Bayesian updating. In the proposed algorithm, the input realizations are firstly treated as observations to construct a likelihood function. Then by Bayesian updating, all conditional output expectations on different input realizations, which are required in estimating VGS and most time-consuming, can be obtained as the posterior and estimated by the sample of simulating the output expectation. The proposed algorithm shares the sample of solving output expectation to obtain all conditional ones required for solving VGS, which makes the computational effort of estimating VGS equivalent to that of estimating output expectation, thus improving the efficiency of estimating VGS. Numerical and engineering examples fully substantiate the novelty and effectiveness of this algorithm. [ABSTRACT FROM AUTHOR]

We provide a unified analysis of dynamically consistent menu preferences in which an agent may exhibit a preference for flexibility, a preference for commitment, or both. Our work generalizes prior results, which investigated this problem for an agent who always exhibits preference for flexibility. By using two types of consistency conditions, we characterize an agent with a subjective state space who reacts to information about her subjective states in a dynamically consistent way. We apply our results to the uncertain strength of temptation and the anticipating regret representations, and characterize a dynamically consistent updating of the no-uncertainty representation. [ABSTRACT FROM AUTHOR]

Site investigation provides essential geotechnical parameter information for analysis and design. However, three conflicting objectives, namely exploration effort, robustness and parameter uncertainty, pose a challenge to the development of an optimal site investigation program. In this study, a three objective optimization framework for the site investigation program is proposed based on the Bayesian approach and the non‐dominated sorting genetic algorithm (NSGA‐II). The only inputs required by the proposed framework are prior distribution of geotechnical parameters and error information. The prior distribution of geotechnical parameters is derived from integrating engineering experience and measurements from basic exploration boreholes. The error information is obtained based on literature and expert judgment related to the specific project. Firstly, a design pool of candidate investigation programs is generated using Bayesian approach to determine the locations and number of exploration boreholes. The NSGA‐II is then applied to identify the optimal program that balances lower cost, higher robustness, and lower uncertainty. The proposed multiobjective optimization framework is illustrated and validated through a real site investigation case in Chongqing, China, aimed at determining the ultimate bearing capacity of the rock foundation. The spatial correlation of parameters within the study area is also considered. The optimal program is represented by the location and number of exploration boreholes. By comparing measurements with predictions from different site investigation programs, the efficiency of the proposed multiobjective framework is demonstrated. Additionally, the influence of engineering experience and random field modeling on the investigation program is discussed. [ABSTRACT FROM AUTHOR]

The evaluation of rebar corrosion in reinforced concrete (RC) structures presents a significant challenge for structural engineers, with on‐site visual inspections and crack width measurements playing a key role in the preliminary assessment. A general approach is to use corrosion models to predict the corrosion level of existing structures, and subsequently update these predictions by means of on‐site data. In this process, empirical models are often applied that relate the observed damage, such as corrosion‐induced cracks, to the corrosion level. However, these models have large uncertainties and are often not applicable to conditions that deviate from the test setups used to derive the empirical relations. This research aims to expand the applicability of these practical, existing empirical models for on‐site corrosion assessment through Bayesian updating techniques. To this aim, a Bayesian framework is developed to update crack width–corrosion models. The Bayesian updating methodology is illustrated for a simple regression model, and for a more complex relation that accounts for additional variables. A novel online database (KUL‐edCCRC) is used that is published accompanying this paper. The obtained results illustrate that the updated models have better agreement with the experimental results, and it is found that the confidence intervals of the regression parameters decrease for an increasing number of observations, even for a small number of additional datapoints. The results hence prove the efficiency of the approach to integrate information from new observations with prior information to adjust the crack width–corrosion model for specific conditions or cases, resulting in a more relevant model as more information becomes available. [ABSTRACT FROM AUTHOR]

Model updating (i.e., inverse problems) involving high-dimension and nonlinearity plays an increasingly important role in various research fields, e.g., digital twin model updating. Bayesian updating has become a robust and rigorous probabilistic means for parameter uncertainty quantification. However, it remains a challenging task to simultaneously ensure theoretically rigorous and computationally efficient solving of inverse problems involving high-dimensionality and nonlinearity. To address the issue, by applying the basic idea of subset simulation (SuS) to the ensemble Kalman filter (EnKF) that is computationally efficient and applicable to high-dimensional inverse problems, we designed an efficient Monte Carlo method, termed ensemble Kalman filter with subset simulation (EnKF-SuS). In EnKF-SuS, SuS provides a rigorous theoretical basis for guiding EnKF to adaptively search and explore the true target space(s), while avoiding the disadvantages of Markov chain Monte Carlo (MCMC)-based sampling methods by utilizing EnKF to generate updated ensemble samples. The performance of EnKF-SuS was validated and analyzed through three case studies involving multimodal posterior, strongly nonlinear, or high-dimensional problems. Moreover, based on a simplified model for longitudinal response analysis of shield tunnels, we present the application of EnKF-SuS in model updating using centrifuge data and field data, respectively. Results indicate that EnKF-SuS can accurately and efficiently sample from general target posterior distributions, especially in the tested strongly nonlinear or high-dimensional problems, and it is computationally one to two orders of magnitude faster than tested with MCMC-sampling methods. [ABSTRACT FROM AUTHOR]

This study aims to establish an objective analytical framework for determining the number of boreholes that are essential for addressing soil slope design challenges in diverse geological/geotechnical settings. This study utilizes the covariance matrix decomposition method and a two-directional one-dimensional Markovian covariance function to create a two-dimensional random field. A Monte Carlo simulation is used to assess the statistical response based on the generated random fields. A random limit equilibrium method (RLEM) code in MATLAB (version R2023a) is developed using circular slip surfaces equipped with a chaotic particle swarm optimization technique for the reliability analysis of soil slopes. Additionally, the strength reduction method based on the finite difference/finite-element (FE) techniques is adopted to compare the reliability analysis results, such as the probability of failure (Pf). A new programming strategy is adopted to simulate the spatial variability in the FE soil slope model and calculate the factor of safety using a gradient of the maximum slope displacement. Bayesian updating is applied to adjust the conditional probabilities of decision variables and the component reliability. The strategic deployment of boreholes at the toe, middle, and top of the slope results in a significant reduction in the estimated Pf according to the RLEM, the random finite difference method (RFDM), and random FEM (RFEM) analyses. However, employing subsequent boreholes does not proportionally decrease the Pf. The influence of the horizontal autocorrelation distance (ACD) on the Pf is explored, showing that as the horizontal ACD increases from 10 to 20 m, the estimated Pf for the three boreholes decreases to 19% and 13% in the RFEM and RLEM, respectively. This reduction becomes less pronounced, dropping to 4% and 1.3%, respectively, when the ACD increases to 30 m. [ABSTRACT FROM AUTHOR]

Probabilistic back-analysis is an important means to infer the statistics of uncertain soil parameters, making the slope reliability assessment closer to the engineering reality. However, multi-source information (including test data, monitored data, field observation and slope survival records) is rarely used in current probabilistic back-analysis. Conducting the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction under rainfalls by integrating multi-source information is a challenging task since thousands of random variables and high-dimensional likelihood function are usually involved. In this paper, a framework by integrating a modified Bayesian Updating with Subset simulation (mBUS) method with adaptive Conditional Sampling (aCS) algorithm is established for the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction. Within this framework, the high-dimensional probabilistic back-analysis problem can be easily tackled, and the multi-source information (e.g. monitored pressure heads and slope survival records) can be fully used in the back-analysis. A real Taoyuan landslide case in Taiwan, China is investigated to illustrate the effectiveness and performance of the established framework. The findings show that the posterior knowledge of soil parameters obtained from the established framework is in good agreement with the field observations. Furthermore, the updated knowledge of soil parameters can be utilized to reliably predict the occurrence probability of a landslide caused by the heavy rainfall event on September 12, 2004 or forecast the potential landslides under future rainfalls in the Fuhsing District of Taoyuan City, Taiwan, China.

In many studies in the social and behavioral sciences, the data have a multilevel structure, with subjects nested within clusters. In the design phase of such a study, the number of clusters to achieve a desired power level has to be calculated. This requires a priori estimates of the effect size and intraclass correlation coefficient. If these estimates are incorrect, the study may be under- or overpowered. This may be overcome by using a group-sequential design, where interim tests are done at various points in time of the study. Based on interim test results, a decision is made to either include additional clusters or to reject the null hypothesis and conclude the study. This contribution introduces Bayesian sequential designs as an alternative to group-sequential designs. This approach compares various hypotheses based on the support in the data for each of them. If neither hypothesis receives a sufficient degree of support, additional clusters are included in the study and the Bayes factor is recalculated. This procedure continues until one of the hypotheses receives sufficient support. This paper explains how the Bayes factor is used as a measure of support for a hypothesis and how a Bayesian sequential design is conducted. A simulation study in the setting of a two-group comparison was conducted to study the effects of the minimum and maximum number of clusters per group and the desired degree of support. It is concluded that Bayesian sequential designs are a flexible alternative to the group sequential design. [ABSTRACT FROM AUTHOR]

We estimate the value of information to farmers from a soybean rust sentinel plot monitoring network. Monitoring provides value over time from farmers learning about their risk of infection and contemporaneously from farmers making more profitable fungicide applications. Optimizing the spatial configuration of the network reveals the value of information plateaus at around $920 million with around 75 sentinel plots. The optimal spatial distribution of sentinel plots differs substantially from historical placements. Optimal sentinel plot placement would have included fewer plots in the Southern United States, where the risk of infection is relatively high, and more in the Midwest, where soybean production is relatively high. [ABSTRACT FROM AUTHOR]

This article provides a precise, quantitative description of the sampling error on burn counts in Monte-Carlo wildfire simulations - that is, the prediction variability introduced by the fact that the set of simulated fires is random and finite. We show that the marginal burn counts are (very nearly) Poisson-distributed in typical settings and infer through Bayesian updating that Gamma distributions are suitable summaries of the remaining uncertainty. In particular, the coefficient of variation of the burn count is equal to the inverse square root of its expected value, and this expected value is proportional to the number of simulated fires multiplied by the asymptotic burn probability. From these results, we derive practical guidelines for choosing the number of simulated fires and estimating the sampling error. Notably, the required number of simulated years is expressed as a power law. Such findings promise to relieve fire modelers of resource-consuming iterative experiments for sizing simulations and assessing their convergence: statistical theory provides better answers, faster. [ABSTRACT FROM AUTHOR]

To predict the future condition of a bridge, statistical models for the time-in-condition rating (TICR) can estimate the time that a bridge stays in a given condition and then predict the future condition of the bridge. However, existing research typically uses the probability density functions as the likelihood function when the TICR is estimated by Bayesian updating, in which the change in the condition rating (CR) between two consecutive inspections is assumed to occur at the later inspection, which ignores the uncertainty of the time of the condition change. This assumption will introduce an error, which is particularly significant when the two consecutive inspections are separated over a long time. In addition, a large amount of existing bridge inspection data in China has not been fully recorded; for instance, a lot of bridge inspection data only contains the CR of the bridge from the last inspection. Current research that is based on the TICR has difficulty using this incomplete data. To solve these difficulties, this paper proposes a probability-based likelihood function for the Bayesian updating of the TICR models, which could estimate the distribution of the TICR more accurately using fully recorded or single data. The accuracy of the proposed method is verified with numerical examples, and the results from different methods are discussed. Then, the effect of using complete and single data are examined. The proposed method is applied to the CR of the superstructures of reinforced concrete bridges in Beijing that uses the real inspection data, and the future deterioration risk is evaluated using the updated TICR models. [ABSTRACT FROM AUTHOR]

Inverse analysis methods are commonly used in braced excavations for improved deformation predictions. This paper proposes a bi‐fidelity ensemble randomized maximum likelihood (BF‐EnRML) method for efficient inverse analyses of deep excavations considering the three‐dimensional effects. The bi‐fidelity (BF) model is developed by the low‐fidelity model (i.e., two‐dimensional finite element model, 2D FEM) and the high‐fidelity model (i.e., 3D FEM) for a balance between efficiency and accuracy. A large number of 2D FEMs are first used to explore the relationship between soil parameters and wall deflections. A few 3D FEMs are then performed to calibrate the discrepancy between 2D‐3D deflections caused by the inability of 2D FEM to consider the three‐dimensional effects. The constructed BF model serves as the forward model in inverse analysis. The soil parameters are updated by incorporating the monitoring data based on EnRML and further used to predict wall deflections in later stages. A hypothetical excavation and a real project are studied to evaluate the performance of the proposed method. The results show that the BF model can provide wall deflection predictions close to those calculated from 3D FEM while using a computational cost of 2D FEM. The BF‐EnRML method can efficiently update the soil parameters and improve the wall deflection predictions. Moreover, factors affecting the accuracy of the BF model are studied, including the number of required 3D FEMs, the distance from the evaluated wall section to the excavation corner, the number of data points along the wall depth, and the number of excavation stages. [ABSTRACT FROM AUTHOR]

Animals often make decisions without perfect knowledge of environmental parameters like the quality of an encountered food patch or a potential mate. Theoreticians often assume animals make such decisions using a Bayesian updating process that combines prior information about the frequency distribution of resources in the environment with sample information from an encountered resource; such a process leads to decisions that maximize fitness, given the available information. I examine three aspects of empirical work that shed light on the idea that animals can make such decisions in a Bayesian‐like manner. First, many animals are sensitive to variance differences in behavioral options, one metric used to characterize frequency distributions. Second, several species use information about the relative frequency of preferred versus nonpreferred items in different populations to make probabilistic inferences about samples taken from populations in a manner that results in maximizing the likelihood of obtaining a preferred reward. Third, the predictions of Bayesian models often match the behavior of individuals in two main approaches. One approach compares behavior to models that make different assumptions about how individuals estimate the quality of an environmental parameter. The patch exploitation behavior of nine species of birds and mammals has matched the predictions of Bayesian models. The other approach compares the behavior of individuals who learn, through experience, different frequency distributions of resources in their environment. The behavior of three bird species and bumblebees exploiting food patches and fruit flies selecting mates is influenced by their experience learning different frequency distributions of food and mates, respectively, in ways consistent with Bayesian models. These studies lend support to the idea that animals may combine prior and sample information in a Bayesian‐like manner to make decisions under uncertainty, but additional work on a greater diversity of species is required to better understand the generality of this ability. I examine empirical work that sheds light on the idea that animals can make decisions in a Bayesian‐like manner. I show evidence that animals are sensitive to variation in reward options and can make probabilistic inferences, two foundations of Bayesian‐like estimation. I also show that Bayesian models often predict the behavior of individuals in different environmental contexts. [ABSTRACT FROM AUTHOR]

Prioritizing multiple potentially unstable slopes based on varying levels of criticality is an essential approach when resources are limited. This study presents a framework for planning the prioritization of unstable rock slope remediation. The prioritization of mitigation measures for these slopes is determined using deformation values derived from indirect information, such as rock classification systems. To demonstrate the application of this framework, a case study involving unstable rock slopes in southwestern China is presented. Initially, the most appropriate estimation model was chosen from among six candidates for estimating the deformation modulus of the unstable rock mass in the slope, utilizing indirect information. Subsequently, the deformation modulus, updated through Bayesian analysis, was employed to generate the corresponding deformation distributions for the rock slopes. Compared to traditional quantitative methods, the proposed framework is adaptable to various working conditions and yields more definitive results. The indirect data used in this framework are cost-effective, enabling the assessment of all unstable slopes. Moreover, the effect of the amount of indirect data and prior information on slope remediation prioritization is discussed. The proposed framework offers a valuable tool for decision-makers in planning and prioritizing mitigation measures.

The probability analysis of ground deformation is becoming a trend to estimate and control the risk brought by shield tunnelling. The gap parameter is regarded as an effective tool to estimate the ground loss of tunnelling in soft soil. More specifically, ω, which is a gap parameter component defined as the over (or insufficient) excavation due to the change in the posture of the shield machine, may contribute more to the uncertainty of the ground loss. However, the existing uncertainty characterization methods for ω have several limitations and cannot explain the uncertain correlations between the relevant parameters. Along these lines, to better characterize the uncertainty of ω, the multivariate probability distribution was developed in this work and a dynamic prediction was proposed for it. To attain this goal, 1 523 rings of the field data coming from the shield tunnel between Longqing Road and Baiyun Road in Kunming Metro Line 5 were utilized and 44 parameters including the construction, stratigraphic, and posture parameters were collected to form the database. According to the variance filter method, the mutual information method, and the value of the correlation coefficients, the original 44 parameters were reduced to 10 main parameters, which were unit weight, the stoke of the jacks (A, B, C, and D groups), the pressure of the pushing jacks (A, C groups), the chamber pressure, the rotation speed, and the total force. The multivariate probability distribution was constructed based on the Johnson system of distributions. Moreover, the distribution was satisfactorily verified in explaining the pairwise correlation between ω and other parameters through 2 million simulation cases. At last, the distribution was used as a prior distribution to update the marginal distribution of ω with any group of the relevant parameters known. The performance of the dynamic prediction was further validated by the field data of 3 shield tunnel cases.

Recently, the application of Bayesian updating to predict excavation-induced deformation has proven successful and improved prediction accuracy significantly. However, updating the ground settlement profile, which is crucial for determining potential damage to nearby infrastructures, has received limited attention. To address this, this paper proposes a physics-guided simplified model combined with a Bayesian updating framework to accurately predict the ground settlement profile. The advantage of this model is that it eliminates the need for complex finite element modeling and makes the updating framework user-friendly. Furthermore, the model is physically interpretable, which can provide valuable references for construction adjustments. The effectiveness of the proposed method is demonstrated through two field case studies, showing that it can yield satisfactory predictions for the settlement profile.

The accurate prediction of short-term platform motions in a real environment is crucial for the safe design, operation, and maintenance of floating offshore wind turbines (FOWTs). Numerical simulations of motions are typically associated with high uncertainties due to abstracted theoretical models, empirical parameters, initial environment parameters, etc. Therefore, it is necessary to integrate other sources of information associated with less uncertainty, e.g., monitoring data, for accurate predictions. In this paper, we propose a probabilistic prediction based on the Bayesian approach that logically integrates motion monitoring data with simulated motion predictions of FOWTs, considering uncertainties in the environment model, structural properties, motion prediction method, monitoring data, etc. The approach consists of constructing a prior probability density function (PDF) of a random variable (which characterizes the largest value of the initial motion response) via numerical simulations and a likelihood function based on platform motion monitoring data and deriving a posterior PDF of the random variable by Bayesian updating. Then, posterior distributions of short-term extreme motion responses are derived using the posterior PDF of the random variable, representing lower uncertainty and improved accuracy. A Metropolis–Hastings algorithm is adopted to obtain PDFs of complex probability distributions. The effectiveness of the approach is demonstrated on a real FOWT platform in Scotland. The proposed probabilistic prediction approach results in posterior distributions of short-term extreme platform motions associated with less uncertainty and higher accuracy, which is attributed to integrating prior knowledge with monitoring data. [ABSTRACT FROM AUTHOR]

Attempts to reintroduce species to managed areas may be compromised by dispersal into the surrounding landscape. Therefore, decisions regarding the selection and ongoing management of reintroduction areas require predicting dispersal as well as the survival and reproduction rates of the species to be reintroduced. Dispersal can potentially be measured directly by tracking animals, but this is often impractical. However, dispersal can also be inferred from re‐sighting surveys done within reintroduction areas if such data are available from multiple areas with varying connectivity to the surrounding landscape, allowing apparent survival and recruitment to be modelled as a function of connectivity metrics. Here, we show how data from 10 previous reintroductions of a New Zealand passerine, the toutouwai (Petroica longipes), were used to predict population dynamics at a predator‐controlled reintroduction area with high connectivity, and predictions then updated using post‐release data. Bayesian hierarchical modelling of the previous data produced prior distributions for productivity, adult survival and apparent juvenile survival rates that accounted for random variation among areas as well as rat density and connectivity. The modelling of apparent juvenile survival as a function of connectivity allowed it to be partitioned into estimates of survival and fidelity. Bayesian updating based on post‐release data produced posterior distributions for parameters that were consistent with the priors but much more precise. The prior data also allowed the recruitment rate estimated in the new area to be partitioned into separate estimates for productivity, juvenile survival and juvenile fidelity. Consequently, it was possible to not only estimate population growth under current management, but also predict the consequences of reducing the scale or intensity of predator control, facilitating adaptive management. The updated model could then be used to predict population growth as a function of the connectivity and predator control regime at proposed reintroduction areas while accounting for random variation among areas. [ABSTRACT FROM AUTHOR]

The post-fire assessment of concrete structures is a complex task that requires the integration of multiple measurements from different techniques. The current approach to integrate information from different sources relies mainly on expert judgement, meaning that no explicit consideration is given to the precision of different techniques. This paper presents a Bayesian updating methodology that integrates information from different sources about the maximum temperature the concrete experienced during fire exposure at a certain depth, such as discoloration and rebound hammer measurements, by considering the uncertainties and errors associated with measurements. The data is then used to update the prior information on the uncertain parameters of interest, here the fire load density and opening factor. These updated distributions provide a better estimate of the fire exposure, thermal and damage gradient and the residual condition of the structure. The proof-of-concept and effectiveness of the proposed methodology are demonstrated through a case study. The results show that the proposed methodology is able to effectively incorporate the uncertainties and errors associated with the assessment techniques, producing more reliable estimates of the fire severity. This method has the potential to improve the post-fire assessment process and provide more accurate information for the rehabilitation of concrete structures. [ABSTRACT FROM AUTHOR]

Electoral disinformation is feared to variously undermine democratic trust by inflaming incorrect negative beliefs about the fairness of elections, or to shore up dictators by creating falsely positive ones. Recent studies of political misperceptions, however, suggest that disinformation has at best minimal effects on beliefs. In this article, we investigate the drivers of public perceptions and misperceptions of election fairness. We build on theories of rational belief updating and motivated reasoning, and link public opinion data from 82 national elections with expert survey data on disinformation and de facto electoral integrity. We show that, overall, people arrive at largely accurate perceptions, but that disinformation campaigns are indeed associated with less accurate and more polarized beliefs about election fairness. This contributes a cross-nationally comparative perspective to studies of (dis)information processing and belief updating, as well as attitude formation and trust surrounding highly salient political institutions such as elections. [ABSTRACT FROM AUTHOR]

Applications of Bayesian updating commonly treat soil parameters as random variables. A significant issue with this is that soil parameters are highly subjective. Therefore, using traditional parameter-based models, Bayesian analysis starts from a subjective prior and it is unclear how this may influence the overall results of a study. In this paper, Bayesian updating is combined with a data-driven method, known as CRACA (i.e., CReep And Consolidation Analysis), for predicting the settlement of embankments on soft soil. Importantly, the method directly ingests measured oedometer data and, therefore, avoids the subjectivity involved in parameter selection. Because parameters are not used, scaling factors are introduced that account uncertainty associated with the laboratory measurements and the automated interpretation process. These factors have an initial value of unity (returning the prior) and are updated in a Bayesian framework as settlement monitoring data are revealed over time to improve future forecasts. The model was applied to an embankment case history and was shown to result in a rapid improvement in the accuracy and a narrowing of the 95% confidence interval as settlement monitoring data are revealed to the model. [ABSTRACT FROM AUTHOR]

When can a shift from a prior to a posterior be represented by conditionalization? A well-known result, known as "superconditioning" and going back to work by Diaconis and Zabell, gives a sharp answer. This paper extends the result and connects it to the reflection principle and common priors. I show that a shift from a prior to a set of posteriors can be represented within a conditioning model if and only if the prior and the posteriors are connected via a general form of the reflection principle. Common priors can be characterized by principles that require a certain kind of coherence between distinct sets of posteriors. I discuss the implications these results have for diachronic and synchronic modes of updating, learning experiences, the common prior assumption of game theory, and time-slice epistemology. [ABSTRACT FROM AUTHOR]

The rapid development of electric vehicles (EVs) has created more possibilities for their flexible participation in electric power dispatching. Considering the clustering and fast mobility of EVs coinciding with real-time market requirements for responsive demands, a bidding strategy is proposed in this paper to assist EV aggregators with submitting reasonable quotations for the real-time market. Based on Bayesian updating, the strategy in this paper considers relevant factors such as aggregator cost and expected return. First, a quotation strategy in electricity spot market is proposed for EV aggregators. Second, the applicable scope of the strategy is given under different scenarios. Then, the objective function of learning is proposed, and the specific process of Bayesian updating is demonstrated. Finally, a case study of the aggregator quotation strategy based on Bayesian updating is simulated to verify the effectiveness and feasibility of the proposed strategy, which helps improve the bidding success rate of EVs and peak-to-valley balance of the grid. [ABSTRACT FROM AUTHOR]

Abstract Animals often make decisions without perfect knowledge of environmental parameters like the quality of an encountered food patch or a potential mate. Theoreticians often assume animals make such decisions using a Bayesian updating process that combines prior information about the frequency distribution of resources in the environment with sample information from an encountered resource; such a process leads to decisions that maximize fitness, given the available information. I examine three aspects of empirical work that shed light on the idea that animals can make such decisions in a Bayesian‐like manner. First, many animals are sensitive to variance differences in behavioral options, one metric used to characterize frequency distributions. Second, several species use information about the relative frequency of preferred versus nonpreferred items in different populations to make probabilistic inferences about samples taken from populations in a manner that results in maximizing the likelihood of obtaining a preferred reward. Third, the predictions of Bayesian models often match the behavior of individuals in two main approaches. One approach compares behavior to models that make different assumptions about how individuals estimate the quality of an environmental parameter. The patch exploitation behavior of nine species of birds and mammals has matched the predictions of Bayesian models. The other approach compares the behavior of individuals who learn, through experience, different frequency distributions of resources in their environment. The behavior of three bird species and bumblebees exploiting food patches and fruit flies selecting mates is influenced by their experience learning different frequency distributions of food and mates, respectively, in ways consistent with Bayesian models. These studies lend support to the idea that animals may combine prior and sample information in a Bayesian‐like manner to make decisions under uncertainty, but additional work on a greater diversity of species is required to better understand the generality of this ability.

Probabilistic inverse-analysis is an essential approach to infer statistical characteristics of uncertain soil parameters, making the slope reliability assessment closer to engineering reality. However, current probabilistic inverse analysis rarely integrates multi-source information, including monitored data, field observation information, and slope survival records. Conducting the probabilistic inverse-analysis of spatially varying soil parameters and slope reliability prediction under rainfalls by integrating the multi-source information is a challenging issue due to the involvement of thousands of random variables and the evaluation of high-dimensional likelihood functions. In this paper, a modified Bayesian updating with subset simulation (mBUS) method is combined with adaptive conditional sampling (aCS) algorithm to establish a framework for probabilistic inverse analysis of spatially variable soil parameters and reliability prediction of slopes. The effectiveness of this framework is validated using a highway slope as a case study. The research results show that the posterior statistical characteristics of soil parameters obtained by integrating multi-source information are in good agreement with field observation results. Additionally, the probability of slope failure under heavy rainfall on September 12, 2004 with the updated soil parameters is 23.1 %, which is in line with the actual slope instability. Within this framework, multi-source information can be fully utilized to address high-dimensional probabilistic inverse analysis problems.

Shapley value-based explainable AI has recently attracted significant interest. However, the computational complexity of the Shapley value grows exponentially with the number of players, resulting in high computational costs that prevent its widespread practical application. To address this challenge, various approximation methods have been proposed in the literature for computing the Shapley value, such as linear Shapley computation, sampling-based Shapley computation, and several estimation-based approaches. Among these methods, the sampling approach exhibits non-zero bias and variance and is sufficiently universal to be used with almost any AI algorithm. However, it suffers from unstable interpretability results and slow convergence in high-dimensional problems. To address these problems, we propose integrating a sequential Bayesian updating framework into the Shapley sampling approach. The core idea of this method is to dynamically update probabilities based on each sample’s Shapley value combined with a selection strategy. Both theoretical analysis and empirical results show that this method significantly improves the convergence speed and interpretability compared to the original sampling approach.

Understanding consciousness is central to understanding human nature. We have competing theories of consciousness. In interdisciplinary consciousness studies most believe that consciousness can be naturalized (i.e., consciousness depends in some substantial way on processes in — or states of — the brain). For roughly two decades, proponents of almost every theory have focused on collecting empirical support for their preferred theory, on the tacit assumption that empirical evidence will resolve the debates. Yet, it remains unclear how empirical evidence can do this in practice. Here I address this issue by offering (a sketch of) a methodology to quantify the divergent sets of empirical support proposed in favor of extant theories of consciousness. This in turn forms the foundation for a process of inference to the best explanation inspired by Bayesian confirmation theory. In interdisciplinary consciousness studies we are blessed with an abundance of theories, but we have reached a point where, going forward, it would be beneficial to focus on the most promising ones. Methods for assessment and comparison are necessary to identify which those are. While future refinement is likely, the methodology for assessment and comparison proposed here is a first step toward a novel way of approaching this through a quantification of empirical support for theories of consciousness. [ABSTRACT FROM AUTHOR]

• Evaluated a multistep mutation model in detail for interpretation of Y-STR profiles. • Proved mathematically that the rate and step parameters can be estimated separately. • Bayesian Beta-Binomial & Beta-Geometric updating for rate & step parameters. • Showed the danger of reporting a parameter point estimate without its variation range. • Showed that there is not enough published data to estimate allele range parameter. If Y-STR profiling is to be more effective in criminal casework, the methods used to evaluate evidential weight require improvement. Many forensic scientists assign an evidential weight by estimating the number of times a Y-STR profile obtained from a questioned sample has been observed in YHRD datasets. More sophisticated models have been suggested but not yet implemented into routine casework, e.g. Andersen & Balding [1]. Mutation is inherent to STR meiosis (or inheritance) and is encountered in practice. We evaluated a mutation model that can be incorporated into a method for assigning evidential weight to Y-STR profiles, an essential part of bringing any method into practice. Since an important part of implementation to casework is communication, the article is written in an accessible format for practitioners as well as statisticians. The mutation component within the MUTEA model by Willems et al. [2] incorporates the potential for multistep mutations and a tendency for alleles to revert towards a central length, reflecting observed mutation data, e.g. [3]. We have estimated the parameters in this model and in a simplified symmetric version of this model, using sequence data from father/son pairs [4] and deep-rooted pedigrees [5]. Both datasets contain multistep mutations, which may have an effect on models based on simulations [1]. We introduce Beta-Binomial and Beta-Geometric conjugate analyses for estimating rate and step parameters for the mutation models presented here, which require only summations and multiplications. We proved mathematically that the parameters can be estimated independently. We show the importance of reporting the variability of the parameters and not only a point estimate. The parameters can be easily incorporated into statistical models, and updated sequentially as more data becomes available. We recommend fuller publication of data to enable the development and evaluation of a wider range of mutation models. [ABSTRACT FROM AUTHOR]

In this paper we explore the absentminded driver problem using two different scenarios. In the first scenario we assume that the driver is capable of reasoning about his degree of absentmindedness before he hits the highway. This leads to a Savage-style model where the states are mutually exclusive and the act-state independence is in place. In the second we employ centred possibilities, by modelling the states (i.e. the events about which the driver is uncertain) as the possible final destinations indexed by a time period. The optimal probability we find for continuing at an exit is different from almost all papers in the literature. In this scenario, act-state independence is still violated, but states are mutually exclusive and the driver arrives at his optimal choice probability via Bayesian updating. We show that our solution is the only one guaranteeing immunity from sure loss via a Dutch strategy, and that – despite initial appearances – it is time consistent. This raises important implications for the rationality of commitment in such scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a new way to determine road profile and detect bridge damage using accelerations from a fleet of passing vehicles. Using off-bridge data, a Bayesian approach updates estimates of the road profile and vehicle properties. The profile elevations and vehicle properties are shown to be insensitive to random noise in acceleration measurements. On-bridge data, with recently updated vehicle properties, are used to estimate bridge damage. Bearing damage and local crack damage in a bridge are simulated. For bearing damage, the results show that this method can quantify the damage level of a bearing and infer other bridge properties. For local crack damage, the levels and the location of the damage are inferred from the simulated measurements. [ABSTRACT FROM AUTHOR]

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Abstract Background The main task of applied sport science is to inform decision-making in sports practice, that is, enabling practitioners to compare the expectable outcomes of different options (e.g. training programs). Main Body The “evidence” provided may range from group averages to multivariable prediction models. By contrast, many decisions are still largely based on the subjective, experience-based judgement of athletes and coaches. While for the research scientist this may seem “unscientific” and even “irrational”, it is important to realize the different perspectives: science values novelty, universal validity, methodological rigor, and contributions towards long-term advancement. Practitioners are judged by the performance outcomes of contemporary, specific athletes. This makes out-of-sample predictive accuracy and robustness decisive requirements for useful decision support. At this point, researchers must concede that under the framework conditions of sport (small samples, multifactorial outcomes etc.) near certainty is unattainable, even with cutting-edge methods that might theoretically enable near-perfect accuracy. Rather, the sport ecosystem favors simpler rules, learning by experience, human judgement, and integration across different sources of knowledge. In other words, the focus of practitioners on experience and human judgement, complemented—but not superseded—by scientific evidence is probably street-smart after all. A major downside of this human-driven approach is the lack of science-grade evaluation and transparency. However, methods are available to merge the assets of data- and human-driven strategies and mitigate biases. Short Conclusion This work presents the challenges of learning, forecasting and decision-making in sport as well as specific opportunities for turning the prevailing “evidence vs. eminence” contrast into a synergy.

The inter-well connectivity calculated from reservoir dynamic production data reflects formation heterogeneity quantitatively. Currently, the calculated inter-well connectivity between pair wells is mainly used as a tool for water flood management but not for quantitative reservoir characterization. This study proposes an innovative, dynamic data integration workflow that can integrate inter-well connectivity with a static reservoir model. In the workflow, the first step is calculating the inter-well connectivity vectors from the reservoir pairwise injector and producer wells. The second step covers interpolation in the domain of interest. The third step is to update the permeability model based on the Bayesian updating method. The result of this study shows that integrating the calculated inter-well connectivity with the static models enhances model reliability and it also provides an insight to deeper geological understanding reflected from dynamic data integration in reservoir modeling.

Understanding consciousness is central to understanding human nature. We have competing theories of consciousness. In interdisciplinary consciousness studies most believe that consciousness can be naturalized (i.e., consciousness depends in some substantial way on processes in — or states of — the brain). For roughly two decades, proponents of almost every theory have focused on collecting empirical support for their preferred theory, on the tacit assumption that empirical evidence will resolve the debates. Yet, it remains unclear how empirical evidence can do this in practice. Here I address this issue by offering (a sketch of) a methodology to quantify the divergent sets of empirical support proposed in favor of extant theories of consciousness. This in turn forms the foundation for a process of inference to the best explanation inspired by Bayesian confirmation theory. In interdisciplinary consciousness studies we are blessed with an abundance of theories, but we have reached a point where, going forward, it would be beneficial to focus on the most promising ones. Methods for assessment and comparison are necessary to identify which those are. While future refinement is likely, the methodology for assessment and comparison proposed here is a first step toward a novel way of approaching this through a quantification of empirical support for theories of consciousness.

Soil spatial variability has essential influence on the reliability of geotechnical structures. Karhunen–Loève (KL) series expansion is an effective approach to characterize such features of soil properties. Acquiring solution for Fredholm integral equation of the second type is a necessary prerequisite; however, the corresponding analytical expressions are only available for limited circumstances. To overcome this challenge, a newly proposed method called Chebyshev–Galerkin–KL expansion was developed to discretize the random fields of soil parameters, from which the approximated eigenvalues and eigenfunctions can be obtained using the Chebyshev orthogonal polynomials of the second kind combined with Galerkin technique. Application of the proposed approach is illustrated through reliability analysis of an unsaturated slope example under different rainfall patterns, where the uncertainty in selection of a "best" soil-water characteristic curve (SWCC) model and statistical uncertainties in SWCC model parameters are taken into account. Results show that the developed approach is feasible to generate random fields with sufficient accuracy. Under a constant rainfall duration, the Advanced pattern may lead to shallow landslide with the highest probability, followed by Intermediate and Delayed. It should be noted that Bayesian inference and determination of optimal SWCC model should be carried out prior to reliability analysis. Otherwise, the landslide risk level would be exaggerated. [ABSTRACT FROM AUTHOR]

Individuals enter police encounters with expectations about how these interactions will unfold. These expectations are often rooted in racialized personal, vicarious, and collective experiences with the police. Bayesian updating posits that the way youth perceive treatment by the police during stops and arrests combines with prior expectations and perceptions to shape current views of the law, whereas subtyping suggests this process differs by race. This study examines intra‐ and interracial variability in these processes using longitudinal survey data from 3,085 Black and White youth. Regardless of race, youth who indicate they were treated with disrespect during police encounters had lower perceptions of procedural justice than did those with no contact, whereas contact perceived as respectful had no significant effects. For White but not Black youth, police encounters rated as "neutral" are associated with more negative views of the police. Other forms of legal socialization are also racialized, including messages conveyed in the media and by parents. Limited evidence exists that prior views of the police moderate the effect of police encounters on procedural justice or that these conditioning effects vary by race. Findings support updating, but race differences do not neatly align with findings expected with updating or subtyping theory. [ABSTRACT FROM AUTHOR]

Background: The main task of applied sport science is to inform decision-making in sports practice, that is, enabling practitioners to compare the expectable outcomes of different options (e.g. training programs). Main Body: The "evidence" provided may range from group averages to multivariable prediction models. By contrast, many decisions are still largely based on the subjective, experience-based judgement of athletes and coaches. While for the research scientist this may seem "unscientific" and even "irrational", it is important to realize the different perspectives: science values novelty, universal validity, methodological rigor, and contributions towards long-term advancement. Practitioners are judged by the performance outcomes of contemporary, specific athletes. This makes out-of-sample predictive accuracy and robustness decisive requirements for useful decision support. At this point, researchers must concede that under the framework conditions of sport (small samples, multifactorial outcomes etc.) near certainty is unattainable, even with cutting-edge methods that might theoretically enable near-perfect accuracy. Rather, the sport ecosystem favors simpler rules, learning by experience, human judgement, and integration across different sources of knowledge. In other words, the focus of practitioners on experience and human judgement, complemented—but not superseded—by scientific evidence is probably street-smart after all. A major downside of this human-driven approach is the lack of science-grade evaluation and transparency. However, methods are available to merge the assets of data- and human-driven strategies and mitigate biases. Short Conclusion: This work presents the challenges of learning, forecasting and decision-making in sport as well as specific opportunities for turning the prevailing "evidence vs. eminence" contrast into a synergy. Key Points: Generally, decision making in sports practice is based on "evidence " from scientific research or on "eminence" based on experience and subjective human judgment. Under the framework conditions of sport—specifically complexity and sparse data—both come with limitations that lead to considerable uncertainty when making decisions in practice. A broader range of strategies, smart strategy combinations and critical evaluation of performance under real-world conditions contribute to better decisions and better outcomes in the field of sport and exercise. [ABSTRACT FROM AUTHOR]

That black and white Americans disagree about the carceral state is well established; why this is the case is much less clear. Drawing on group hierarchy theory and the state's role in perpetuating group subordination/domination, we theorize that differences in socialization and contact during emergent adulthood produce divergent priors for racial groups and gender subgroups within race. These different starting points shape how people integrate new information from recent contact into their belief systems. Using a survey of over 11,000 respondents, we find that, instead of all groups integrating information the same way, recent direct contact contributes most to negative attitudes among groups whose contact with government agents is least negatively valenced. While interactions with the American carceral state divide opinions considerably among white Americans and women, adulthood contact for black Americans, especially black men, appears to be but 'a drop in the ocean' of political life. [ABSTRACT FROM AUTHOR]

The uncertainty in the concrete compressive strength is one of the most challenging issues in safety checking of existing reinforced concrete (RC) buildings. The concrete compressive strength used in the assessment can highly influence the vulnerability results and thus the retrofit strategies. The need to use less expensive and less invasive in situ measurements such as the non-destructive tests should be balanced with a careful check of their structural reliability. The compressive concrete strength is characterized herein based on a large database of both in situ destructive and non-destructive results measured on the same structural members. The data are obtained from existing RC buildings mainly located in the Campania region, Southern Italy. Probabilistic linear and multilinear regression models are developed for calculating the compressive concrete strength based on non-destructive tests. Furthermore, the implementation of the concrete strength based on ultrasonic test results are investigated together with the relative measurement error through a fully probabilistic workflow. Accordingly, the relative weights of non-destructive data for calculating concrete compressive strength are estimated and compared with those recommended by the Italian national code. The results demonstrate that the effective weights of the non-destructive data are very close to the code-based recommendation. [ABSTRACT FROM AUTHOR]

Online auction markets host a large number of transactions every day. The transaction data in auction markets are useful for understanding the buyers and sellers in the market. Previous research has shown that sellers with different levels of reputation, as shown by the ratings and comments left in feedback systems, enjoy different levels of price premiums for their transactions. Feedback scores and feedback texts have been shown to correlate with buyers' level of trust in a seller and the price premium that buyers are willing to pay (Ba and Pavlou 2002; Pavlou and Dimoka 2006). However, existing models do not consider the time-order effect, which means that feedback posted more recently may be considered more important than feedback posted less recently. This paper addresses this shortcoming by (1) testing the existence of the time-order effect, and (2) proposing a Bayesian updating model to represent buyers' perceived reputation considering the time-order effect and assessing how well it can explain the variation in buyers' trust and price premiums. In order to validate the time-order effect and evaluate the proposed model, we conducted a user experiment and collected real-life transaction data from the eBay online auction market. Our results confirm the existence of the time-order effect and the proposed model explains the variation in price premiums better than the benchmark models. The contribution of this research is threefold. First, we verify the timeorder effect in the feedback mechanism on price premiums in online markets. Second, we propose a model that provides better explanatory power for price premiums in online auction markets than existing models by incorporating the time-order effect. Third, we provide further evidence for trust building via textual feedback in online auction markets. The study advances the understanding of the feedback mechanism in online auction markets. [ABSTRACT FROM AUTHOR]

A consumer's decision to engage in search depends on the beliefs the consumer has about an unknown product characteristic (e.g., price). Because beliefs are rarely observed, researchers typically assume that consumers have rational expectations or update beliefs consistent with Bayesian updating. These assumptions are restrictive and do not enable the researcher or the retailer to price discriminate among consumers on the basis of heterogeneity in beliefs. The authors use Monte Carlo experiments to show how these assumptions affect estimates of search cost. Next, they design an incentive-aligned online study in which participants search over the price of a homogeneous good, and the authors elicit distributions of price beliefs before and after each search. Drawing on data collected from a nationally representative panel, they find substantial heterogeneity in prior price beliefs, such that participants update their beliefs in response to search outcomes but deviate from Bayesian updating in that they underreact to new information. Importantly, the authors show that (1) assuming Bayesian updating does not significantly bias search cost estimates at the aggregate level if the researcher accounts for heterogeneous prior beliefs, (2) eliciting heterogeneity in prior expected prices is much more important than eliciting heterogeneity in prior price uncertainty, and (3) a retailer can increase profits through third-degree price discrimination by recognizing the heterogeneity in prior beliefs. [ABSTRACT FROM AUTHOR]