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Deep learning excels at managing spatial and temporal time series with variable patterns for streamflow forecasting, but traditional machine learning algorithms may struggle with complicated data, including non-linear and multidimensional complexity. Empirical heterogeneity within watersheds and limitations inherent to each estimation methodology pose challenges in effectively measuring and appraising hydrological statistical frameworks of spatial and temporal variables. This study emphasizes streamflow forecasting in the region of Johor, a coastal state in Peninsular Malaysia, utilizing a 28-year streamflow-pattern dataset from Malaysia’s Department of Irrigation and Drainage for the Johor River and its tropical rainforest environment. For this dataset, wavelet transformation significantly improves the resolution of lag noise when historical streamflow data are used as lagged input variables, producing a 6% reduction in the root-mean-square error. A comparative analysis of convolutional neural networks and artificial neural networks reveals these models’ distinct behavioral patterns. Convolutional neural networks exhibit lower stochasticity than artificial neural networks when dealing with complex time series data and with data transformed into a format suitable for modeling. However, convolutional neural networks may suffer from overfitting, particularly in cases in which the structure of the time series is overly simplified. Using Bayesian neural networks, we modeled network weights and biases as probability distributions to assess aleatoric and epistemic variability, employing Markov chain Monte Carlo and bootstrap resampling techniques. This modeling allowed us to quantify uncertainty, providing confidence intervals and metrics for a robust quantitative assessment of model prediction variability.

The cingulate cortex is known to be a complex structure, involved in several cognitive and emotional functions, as well as being altered by a variety of brain disorders. This heterogeneity is reflected in the multiple parceling models proposed in the literature. At the present, sub-regions of the cingulate cortex had been identified taking into account functional and structural connectivity, as well as cytological and electrochemical properties. In the present work, we propose an innovative node-wise parceling approach based on meta-analytic Bayesian co-alteration. To this aim, 193 case–control voxel-based morphometry experiments were analyzed, and the Patel's κ index was used to assess probability of morphometric co-alteration between nodes placed in the cingulate cortex and in the rest of the brain. Hierarchical clustering was then applied to identify nodes in the cingulate cortex exhibiting a similar pattern of whole-brain co-alteration. The obtained dendrogram highlighted a robust fronto-parietal cluster compatible with the default mode network, and being supported by the interplay between the retrosplenial cortex and the anterior and posterior cingulate cortex, rarely described in the literature. This ensemble was further confirmed by the analysis of functional patterns. Leveraging on co-alteration to investigate cortical organization could, therefore, allow to combine multimodal information, resolving conflicting results sometimes coming from the separate use of singular modalities. Crucially, this provides a valuable way to understand the pathological brain using data driven, whole-brain informed and context-specific evidence in a way not yet explored in the field. [ABSTRACT FROM AUTHOR]

Failure angle, also called maximum safe slope angle, is an important design parameter for rock slope stability analysis and slope excavation. This study aims to scientifically acquire the failure angle of the jointed rock slope, and a method incorporating the kinematic analysis, Bayesian estimation and Monte Carlo simulation is proposed. The probabilistic kinematic analysis could consider the variety of discontinuity properties. The Bayesian estimation and Monte Carlo simulation could consider the varieties and uncertainties of the failure angle data. The proposed method is applied to a discontinuity-controlled rock slope located in the Fusong County, Jilin Province, China. Numerous widely scattered failure angle data is firstly obtained using the probabilistic kinematic analysis. Then the Bayesian estimation and Monte Carlo simulation are applied to determine the failure angle scientifically. The suggested failure angle of west, middle and east section of the studied slope are 65.67°, 65.79° and 64.76°, respectively. The probabilities of kinematic instabilities are also obtained based on the failure angle data. In general, the studied slope is stable and suitable to develop projects. [ABSTRACT FROM AUTHOR]

This article publishes a new series of radiocarbon dates from Tell Yunatsite, Southern Bulgaria. Context-based excavations undertaken over a large surface area, as well as a small test trench, provided a long stratigraphic sequence (11 ‘building levels’) covering a large part of the Chalcolithic period in Thrace (5th millennium BCE). Bayesian statistics and Gaussian Monte Carlo Wiggle Matching were employed to achieve a fine chronology for the multilayered tell. Implications and problems on the application of the calibration curve for the Late and Final Chalcolithic in Bulgaria are also discussed.

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Purpose: In the context of quality assurance in intensity modulated radiation therapy (IMRT), the aim of this work was two‐fold: (a) to show that the beta distribution characterizes the two‐dimensional gamma index pass rate (GIPR), and that the quantiles of the distribution should be used in order to compute the control limit (CL) for the detection of abnormally low GIPR, and (b) to introduce a Bayesian control chart that allows calculation of CLs from the first measurement. Methods: In order to enable monitoring of the GIPR from the first measurement, we developed a Bayesian control chart based on the beta distribution, elaborated according to the following two steps: (a) an iterative bayesian inference approach without any prior information on the GIPR distribution was used at the start of monitoring and the CL was progressively updated; and (b) when sufficient in‐control arcs had been recorded and the estimators of the parameters of the beta distribution were sufficiently accurate, the CL of the chart was fixed to a constant value corresponding to the quantile of the beta distribution. The clinical utility of this approach is illustrated through a real data case study: monitoring the GIPR of patients treated with a moving gantry IMRT technique RapidArcTM on a Novalis TrueBeam STx (Varian Medical Systems) linear accelerator equipped with an aS1200 electronic portal imager device. Results: We showed that some commonly used distributions for monitoring GIPR in the literature, such as normal or logarithm transformation, are not appropriate. We compared the CLs of those solutions with the CL of our chart based on the BD (CL = 95.14%). The comparison revealed that the CL for the normal law (CL = 97.62%) generated too many false positives, and that the CL of the Logarithm transformation (CL = 83.74%) could fail to efficiently detect (i.e., sufficiently early on or faster) changes in the process. Conclusions: Successful GIPR monitoring requires careful and rigorous application of well‐established statistical concepts in the field of statistical process control. In this paper, we stress the importance of carefully analyzing the distribution of the monitored characteristic that is plotted on the control chart. We propose a Bayesian control chart that can be viewed as a practical solution for early implementation of GIPR monitoring, starting from the first arc. We demonstrate that beta distribution is a better method for characterizing the GIPR, and thus, the use of this approach is expected to improve patient‐specific quality assurance plans in radiotherapy. [ABSTRACT FROM AUTHOR]

This article investigates the problem of modeling the trend of the current Coronavirus disease 2019 pandemic in Lebanon along time. Two different models were developed using Bayesian Markov chain Monte Carlo simulation methods. The models fitted included Poisson autoregressive as a function of a short-term dependence only and Poisson autoregressive as a function of both a short-term dependence and a long-term dependence. The two models are compared in terms of their predictive ability using mean predictions, root mean squared error, and deviance information criterion. The Poisson autoregressive model that allows capturing both short-term and long-term components performs best under all criterions. The use of such a model can greatly improve the estimation of number of new infections, and can indicate whether disease has an upward/downward trend, and where about every country is on that trend, so that containment measures can be applied and/or relaxed. The Bayesian model is flexible in characterizing the uncertainty in the model outputs. The model is also applicable to other countries and more time periods as data becomes available. Further research is encouraged.

Climate change has profound ecological effects, yet our understanding of how trophic interactions among species are affected by climate change is still patchy. The sympatric Atlantic haddock and cod are co‐occurring across the North Atlantic. They compete for food at younger stages and thereafter the former is preyed by the latter. Climate change might affect the interaction and coexistence of these two species. Particularly, the increase in sea temperature (ST) has been shown to affect distribution, population growth and trophic interactions in marine systems. We used 33‐year long time series of haddock and cod abundances estimates from two data sources (acoustic and trawl survey) to analyse the dynamic effect of climate on the coexistence of these two sympatric species in the Arcto‐Boreal Barents Sea. Using a Bayesian state‐space threshold model, we demonstrated that long‐term climate variation, as expressed by changes of ST, affected species demography through different influences on density‐independent processes. The interaction between cod and haddock has shifted in the last two decades due to an increase in ST, altering the equilibrium abundances and the dynamics of the system. During warm years (ST over ca. 4°C), the increase in the cod abundance negatively affected haddock abundance while it did not during cold years. This change in interactions therefore changed the equilibrium population size with a higher population size during warm years. Our analyses show that long‐term climate change in the Arcto‐Boreal system can generate differences in the equilibrium conditions of species assemblages. [ABSTRACT FROM AUTHOR]

Learning automata (LA), which intellectually explores its optimal state by interacting with an external environment continuously, is encountered widely in artificial intelligence. In the evaluation of LA, it has always been a key issue how to tradeoff between “accuracy” and “speed,” which substantially touches on parameter tuning. A latest issue in the design of LA methodology involves bearing a parameter-free property, thus removing the tremendous expenses brought by parameter tuning. Nevertheless, the currently existing parameter-free LA schemes generally maintain a Monte-Carlo technique, which helps avoid the tuning process at the cost of more computations. This paper examines a new measurement of parameter-free LA schemes based on statistics which overcome the difficulties found in other counterparts. Specifically, it has innovatively disengaged from the dependance on Monte-Carlo methods. Of greater significance, the learning mechanisms operating in the common stationary environments are likewise extended to the nonstationary environments. Simulations confirm the effectiveness and efficiency of the proposed algorithm, especially its low computation consumption as well as the strong tracking capability to abrupt environmental changes. [ABSTRACT FROM AUTHOR]

In this paper a Bayesian method is proposed to estimate dynamic origin–destination (O–D) demand. The proposed method can synthesize multiple sources of data collected by various sensors, including link counts, turning movements at intersections, flows, and travel times on partial paths. Time-dependent demand for each O–D pair at each departure time is assumed to satisfy the normal distribution. The connections among multiple sources of field data and O–D demands for all departure times are established by their variance-covariance matrices. Given the prior distribution of dynamic O–D demands, the posterior distribution is developed by updating the traffic count information. Then, based on the posterior distribution, both point estimation and the corresponding confidence intervals of O–D demand variables are estimated. Further, a stepwise algorithm that can avoid matrix inversion, in which traffic counts are updated one by one, is proposed. Finally, a numerical example is conducted on Nguyen–Dupuis network to demonstrate the effectiveness of the proposed Bayesian method and solution algorithm. Results show that the total O–D variance is decreasing with each added traffic count, implying that updating traffic counts reduces O–D demand uncertainty. Using the proposed method, both total error and source-specific errors between estimated and observed traffic counts decrease by iteration. Specifically, using 52 multiple sources of traffic counts, the relative errors of almost 50% traffic counts are less than 5%, the relative errors of 85% traffic counts are less than 10%, the total error between the estimated and “true” O–D demands is relatively small, and the O–D demand estimation accuracy can be improved by using more traffic counts. It concludes that the proposed Bayesian method can effectively synthesize multiple sources of data and estimate dynamic O–D demands with fine accuracy.

The dissemination of the activity is performed from radionuclide sources prepared in a sequence of dilutions and weighing methods. In this process, the activity of the source can be estimated statistically from the deposited mass and the activity concentration of the master solution. After preparation, the activity is obtained from absolute or relative measurement methods. However, the methods of activity determinations used may not fulfill the necessary independence of the conventional statistical approach due to the presence of possible correlations between activities that arise with the use of the same standardized sources or with the same method of quantity measurements. In this paper, Bayesian estimates for the relative deviation of activities and their uncertainty were obtained in order to evaluate the performance of the main sources’ preparation method. The estimate result (0.55 ± 0.27) % for a data set of radionuclide standardization performed between 2017 and 2018 at LNMRI, although close to zero, shows one should investigate possible effects affecting the preparation and measurement of the sources. This Bayesian estimate was validated by monte carlo simulation method.

Copyright of Comunicaciones en Estadística is the property of Universidad Santo Tomas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)