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We study belief updating about relative performance in an ego-relevant task. Manipulating the perceived ego relevance of the task, we show that subjects substantially overweight positive information relative to negative information because they derive direct utility from holding positive beliefs. This finding provides a behavioral explanation why and how overconfidence can evolve in the presence of objective information. Moreover, we document that subjects who receive more negative information downplay the ego relevance of the task. These findings suggest that subjects use two alternative strategies to protect their ego when presented with objective information. This paper was accepted by Marie Claire Villeval, behavioral economics and decision analysis. Funding: The authors gratefully acknowledge financial support from the ExperimenTUM. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2023.02537. [ABSTRACT FROM AUTHOR]

Summary: Semi‐supervised learning is being extensively applied to estimate classifiers from training data in which not all the labels of the feature vectors are available. We present gmmsslm, an R package for estimating the Bayes' classifier from such partially classified data in the case where the feature vector has a multivariate Gaussian (normal) distribution in each of the pre‐defined classes. Our package implements a recently proposed Gaussian mixture modelling framework that incorporates a missingness mechanism for the missing labels in which the probability of a missing label is represented via a logistic model with covariates that depend on the entropy of the feature vector. Under this framework, it has been shown that the accuracy of the Bayes' classifier formed from the Gaussian mixture model fitted to the partially classified training data can even have lower error rate than if it were estimated from the sample completely classified. This result was established in the particular case of two Gaussian classes with a common covariance matrix. Here we focus on the effective implementation of an algorithm for multiple Gaussian classes with arbitrary covariance matrices. A strategy for initialising the algorithm is discussed and illustrated. The new package is demonstrated on some real data. [ABSTRACT FROM AUTHOR]

The focus of this paper is impulse noise reduction in digital color images. The most popular noise reduction schemes are the vector median filter and its many variants that operate by minimizing the aggregate distance from one pixel to every other pixel in a chosen window. This minimizing operation determines the most confirmative pixel based on its similarity to the chosen window and replaces the central pixel of the window with the determined one. The peer group filters, unlike the vector median filters, determine a set of pixels that are most confirmative to the window and then perform filtering over the determined set. Using a set of pixels in the filtering process rather than one pixel is more helpful as it takes into account the full information of all the pixels that seemingly contribute to the signal. Hence, the peer group filters are found to be more robust to noise. However, the peer group for each pixel is computed deterministically using thresholding schemes. A wrong choice of the threshold will easily impair the filtering performance. In this paper, we propose a peer group filtering approach using principles of Bayesian probability theory and clustering. Here, we present a method to compute the probability that a pixel value is clean (not corrupted by impulse noise) and then apply clustering on the probability measure to determine the peer group. The key benefit of this proposal is that the need for thresholding in peer group filtering is completely avoided. Simulation results show that the proposed method performs better than the conventional vector median and peer group filtering methods in terms of noise reduction and structural similarity, thus validating the proposed approach. [ABSTRACT FROM AUTHOR]

In vehicular networks, diverse safety information can be shared among vehicles through internet connections. In vehicle-to-internet communications, vehicles on the road are wirelessly connected to different cloud networks, thereby accelerating safety information exchange. Onboard sensors acquire traffic-related information, and reliable intermediate nodes and network services, such as navigational facilities, allow to transmit safety information to distant target vehicles and stations. Using vehicle-to-network communications, we minimize delays and achieve high accuracy through consistent connectivity links. Our proposed approach uses intermediate nodes with two-hop separation to forward information. Target vehicle detection and routing of safety information are performed using machine learning algorithms. Compared with existing vehicle-to-internet solutions, our approach provides substantial improvements by reducing latency, packet drop, and overhead.

In vehicular networks, diverse safety information can be shared among vehicles through internet connections. In vehicle‐to‐internet communications, vehicles on the road are wirelessly connected to different cloud networks, thereby accelerating safety information exchange. Onboard sensors acquire traffic‐related information, and reliable intermediate nodes and network services, such as navigational facilities, allow to transmit safety information to distant target vehicles and stations. Using vehicle‐to‐network communications, we minimize delays and achieve high accuracy through consistent connectivity links. Our proposed approach uses intermediate nodes with two‐hop separation to forward information. Target vehicle detection and routing of safety information are performed using machine learning algorithms. Compared with existing vehicle‐to‐internet solutions, our approach provides substantial improvements by reducing latency, packet drop, and overhead. [ABSTRACT FROM AUTHOR]

Using a separating equilibrium game model, this paper defines the ratio of cooperators to total individuals for the level of school-enterprise cooperation. We use Bayes’ rule to modify the strategy type and solve the vocational education discrete problem by describing the set of six tuples in the separating equilibrium game model under incomplete information. Transforming the vectors in the symmetric positive definite matrix into complementary linear vectors, it is found that the level of cooperation in the split equilibrium game model increases by about 45%. The reorganization, of course, teaching content in higher education according to the split equilibrium game model can effectively mobilize the learning enthusiasm of most students and provide new ideas for the innovative development of the higher education business.

Researchers have found evidence of contextual bias in forensic science, but the discussion of contextual bias is currently qualitative. We formalise existing empirical research and show quantitatively how biases can be propagated throughout the legal system, all the way up to the final determination of guilt in a criminal trial. We provide a probabilistic framework for describing how information is updated in a forensic analysis setting by using the ratio form of Bayes' rule. We analyse results from empirical studies using this framework and employ simulations to demonstrate how bias can be compounded where experiments do not exist. We find that even minor biases in the earlier stages of forensic analysis can lead to large, compounded biases in the final determination of guilt in a criminal trial. [ABSTRACT FROM AUTHOR]

Many researchers have used sound sensors to record audio data from insects, and used these data as inputs of machine learning algorithms to classify insect species. In image classification, the convolutional neural network (CNN), a well-known deep learning algorithm, achieves better performance than any other machine learning algorithm. This performance is affected by the characteristics of the convolution filter (ConvFilter) learned inside the network. Furthermore, CNN performs well in sound classification. Unlike image classification, however, there is little research on suitable ConvFilters for sound classification. Therefore, we compare the performances of three convolution filters, 1D-ConvFilter, 3×1 2D-ConvFilter, and 3×3 2D-ConvFilter, in two different network configurations, when classifying mosquitoes using audio data. In insect sound classification, most machine learning researchers use only audio data as input. However, a classification model, which combines other information such as activity circadian rhythm, should intuitively yield improved classification results. To utilize such relevant additional information, we propose a method that defines this information as a priori probabilities and combines them with CNN outputs. Of the networks, VGG13 with 3×3 2D-ConvFilter showed the best performance in classifying mosquito species, with an accuracy of 80.8%. Moreover, adding activity circadian rhythm information to the networks showed an average performance improvement of 5.5%. The VGG13 network with 1D-ConvFilter achieved the highest accuracy of 85.7% with the additional activity circadian rhythm information.

Philosophers frequently define knowledge as justified, true belief. We built a mathematical framework that makes it possible to define learning (increasing number of true beliefs) and knowledge of an agent in precise ways, by phrasing belief in terms of epistemic probabilities, defined from Bayes' rule. The degree of true belief is quantified by means of active information I + : a comparison between the degree of belief of the agent and a completely ignorant person. Learning has occurred when either the agent's strength of belief in a true proposition has increased in comparison with the ignorant person ( I + > 0 ), or the strength of belief in a false proposition has decreased ( I + < 0 ). Knowledge additionally requires that learning occurs for the right reason, and in this context we introduce a framework of parallel worlds that correspond to parameters of a statistical model. This makes it possible to interpret learning as a hypothesis test for such a model, whereas knowledge acquisition additionally requires estimation of a true world parameter. Our framework of learning and knowledge acquisition is a hybrid between frequentism and Bayesianism. It can be generalized to a sequential setting, where information and data are updated over time. The theory is illustrated using examples of coin tossing, historical and future events, replication of studies, and causal inference. It can also be used to pinpoint shortcomings of machine learning, where typically learning rather than knowledge acquisition is in focus. [ABSTRACT FROM AUTHOR]

COVID‐19 pandemic policies requiring disease testing provide a rich context to build insights on true positives versus false positives. Our main contribution to the pedagogy of data analytics and statistics is to propose a method for teaching updating of probabilities using Bayes' rule reasoning to build understanding that true positives and false positives depend on the prior probability. Our instructional approach has three parts. First, we show how to construct and interpret raw frequency data tables, instead of using probabilities. Second, we use dynamic visual displays to develop insights and help overcome calculation avoidance or errors. Third, we look at graphs of positive predictive values and negative predictive values for different priors. The learning activities we use include lectures, in‐class discussions and exercises, breakout group problem solving sessions, and homework. Our research offers teaching methods to help students understand that the veracity of test results depends on the prior probability as well as helps students develop fundamental skills in understanding probabilistic uncertainty alongside higher‐level analytical and evaluative skills. Beyond learning to update the probability of having the disease given a positive test result, our material covers naïve estimates of the positive predictive value, the common mistake of ignoring the disease's base rate, debating the relative harm from a false positive versus a false negative, and creating a new disease test. [ABSTRACT FROM AUTHOR]

Bayesian Reasoning is both a fundamental idea of probability and a key model in applied sciences for evaluating situations of uncertainty. Bayesian Reasoning may be defined as the dealing with, and understanding of, Bayesian situations. This includes various aspects such as calculating a conditional probability (performance), assessing the effects of changes to the parameters of a formula on the result (covariation) and adequately interpreting and explaining the results of a formula (communication). Bayesian Reasoning is crucial in several non-mathematical disciplines such as medicine and law. However, even experts from these domains struggle to reason in a Bayesian manner. Therefore, it is desirable to develop a training course for this specific audience regarding the different aspects of Bayesian Reasoning. In this paper, we present an evidence-based development of such training courses by considering relevant prior research on successful strategies for Bayesian Reasoning (e.g., natural frequencies and adequate visualizations) and on the 4C/ID model as a promising instructional approach. The results of a formative evaluation are described, which show that students from the target audience (i.e., medicine or law) increased their Bayesian Reasoning skills and found taking part in the training courses to be relevant and fruitful for their professional expertise. [ABSTRACT FROM AUTHOR]

Bernoulli's 1713 golden theorem is viewed retrospectively in the context of modern model-based frequentist inference that revolves around the concept of a prespecified statistical model M θ x , defining the inductive premises of inference. It is argued that several widely-accepted claims relating to the golden theorem and frequentist inference are either misleading or erroneous: (a) Bernoulli solved the problem of inference 'from probability to frequency', and thus (b) the golden theorem cannot justify an approximate Confidence Interval (CI) for the unknown parameter θ , (c) Bernoulli identified the probability P A with the relative frequency 1 n ∑ k = 1 n x k of event A as a result of conflating f (x 0 | θ) with f (θ | x 0) , where x 0 denotes the observed data, and (d) the same 'swindle' is currently perpetrated by the p value testers. In interrogating the claims (a)–(d), the paper raises several foundational issues that are particularly relevant for statistical induction as it relates to the current discussions on the replication crises and the trustworthiness of empirical evidence, arguing that: [i] The alleged Bernoulli swindle is grounded in the unwarranted claim θ ^ n x 0 ≃ θ ∗ , for a large enough n, where θ ^ n X is an optimal estimator of the true value θ ∗ of θ. [ii] Frequentist error probabilities are not conditional on hypotheses (H0 and H1) framed in terms of an unknown parameter θ since θ is neither a random variable nor an event. [iii] The direct versus inverse inference problem is a contrived and misplaced charge since neither conditional distribution f (x 0 | θ) and f (θ | x 0) exists (formally or logically) in model-based ( M θ x ) frequentist inference. [ABSTRACT FROM AUTHOR]

Edge detection is an important problem in image processing, especially for mixed noise. In this work, we propose a variational edge detection model with mixed noise by using Maximum A-Posteriori (MAP) approach. The novel model is formed with the regularization terms and the data fidelity terms that feature different mixed noise. Furthermore, we adopt the alternating direction method of multipliers (ADMM) to solve the proposed model. Numerical experiments on a variety of gray and color images demonstrate the efficiency of the proposed model. [ABSTRACT FROM AUTHOR]

Philosophers frequently define knowledge as justified, true belief. We built a mathematical framework that makes it possible to define learning (increasing number of true beliefs) and knowledge of an agent in precise ways, by phrasing belief in terms of epistemic probabilities, defined from Bayes’ rule. The degree of true belief is quantified by means of active information I+: a comparison between the degree of belief of the agent and a completely ignorant person. Learning has occurred when either the agent’s strength of belief in a true proposition has increased in comparison with the ignorant person (I+>0), or the strength of belief in a false proposition has decreased (I+<0). Knowledge additionally requires that learning occurs for the right reason, and in this context we introduce a framework of parallel worlds that correspond to parameters of a statistical model. This makes it possible to interpret learning as a hypothesis test for such a model, whereas knowledge acquisition additionally requires estimation of a true world parameter. Our framework of learning and knowledge acquisition is a hybrid between frequentism and Bayesianism. It can be generalized to a sequential setting, where information and data are updated over time. The theory is illustrated using examples of coin tossing, historical and future events, replication of studies, and causal inference. It can also be used to pinpoint shortcomings of machine learning, where typically learning rather than knowledge acquisition is in focus.

In gasoline engines, the spark timing is often advanced to increase fuel economy under certain heavy load engine operating conditions. As a compromise between the risk of knock and the power output, spark timing is regulated at the boundary where a low knock probability is tolerated. Due to the stochasticity of binary knock events, it is necessary to have a large number of engine cycles for probability estimations, which can slow down the response speed of a controller to operating condition changes. To speed up the spark timing regulation and to reduce the spark timing variance, in this article, a knock probability feedforward map learning method and a spark timing control method are proposed under a unified framework. A learning method that applies the beta distribution is the key contribution of this work. The beta distribution in the map learning part is used to describe knock probabilities with uncertainties and to determine the next engine operating condition for sampling and map learning. In the spark timing method, the beta distribution is applied in the conventional control method to adjust the control gains. The proposed methods are experimentally validated on a test bench equipped with a production Toyota 1.8 L, 4-cylinder SI engine. [ABSTRACT FROM AUTHOR]

Bayesian Reasoning is both a fundamental idea of probability and a key model in applied sciences for evaluating situations of uncertainty. Bayesian Reasoning may be defined as the dealing with, and understanding of, Bayesian situations. This includes various aspects such as calculating a conditional probability (performance), assessing the effects of changes to the parameters of a formula on the result (covariation) and adequately interpreting and explaining the results of a formula (communication). Bayesian Reasoning is crucial in several non-mathematical disciplines such as medicine and law. However, even experts from these domains struggle to reason in a Bayesian manner. Therefore, it is desirable to develop a training course for this specific audience regarding the different aspects of Bayesian Reasoning. In this paper, we present an evidence-based development of such training courses by considering relevant prior research on successful strategies for Bayesian Reasoning (e.g., natural frequencies and adequate visualizations) and on the 4C/ID model as a promising instructional approach. The results of a formative evaluation are described, which show that students from the target audience (i.e., medicine or law) increased their Bayesian Reasoning skills and found taking part in the training courses to be relevant and fruitful for their professional expertise.

Bayes' statistical rule remains the status quo for modeling belief updating in both normative and descriptive models of behavior under uncertainty. Some recent research has questioned the use of Bayes' rule in descriptive models of behavior, presenting evidence that people overweight 'good news' relative to 'bad news' when updating ego-relevant beliefs. In this paper, we present experimental evidence testing whether this 'good-news, bad-news' effect is present in a financial decision making context (i.e. a domain that is important for understanding much economic decision making). We find no evidence of asymmetric updating in this domain. In contrast, in our experiment, belief updating is close to the Bayesian benchmark on average. However, we show that this average behavior masks substantial heterogeneity in individual updating behavior. We find no evidence in support of a sizeable subgroup of asymmetric updators. [ABSTRACT FROM AUTHOR]

The fuel efficiency and power output of spark ignition (SI) engines are closely related to the spark timing. Advancing the spark timing is usually used as an approach to increase the efficiency. However, under some operating conditions, advanced spark timing can trigger abnormal combustion, which causes knocking. To avoid cylinder damage and to increase the engine efficiency, feedback control, which addresses the knocking phenomenon as a stochastic process, is required. In this brief, a Bayesian estimate of knock probability is used to replace the maximum likelihood estimate in a likelihood-ratio-based knock control strategy. The beta distribution is used to represent the distribution of the knock probability estimate based on the independent and identically distributed property of knock events. The proposed control algorithm is validated on a full-scale test bench with a production SI engine and is compared with the conventional spark advance control approach and the maximum-likelihood-based approach. The results show that the proposed approach is able to control and maintain a knock probability close to the target and introduce a low dispersion of spark timing after convergence. [ABSTRACT FROM AUTHOR]

A sense of agency (SoA) is the experience of subjective awareness regarding the control of one's actions. Humans have a natural tendency to generate prediction models of the environment and adapt their models according to changes in the environment. The SoA is associated with the degree of the adaptation of the prediction models, e.g., insufficient adaptation causes low predictability and lowers the SoA over the environment. Thus, identifying the mechanisms behind the adaptation process of a prediction model related to the SoA is essential for understanding the generative process of the SoA. In the first half of the current study, we constructed a mathematical model in which the SoA represents a likelihood value for a given observation (sensory feedback) in a prediction model of the environment and in which the prediction model is updated according to the likelihood value. From our mathematical model, we theoretically derived a testable hypothesis that the prediction model is updated according to a Bayesian rule or a stochastic gradient. In the second half of our study, we focused on the experimental examination of this hypothesis. In our experiment, human subjects were repeatedly asked to observe a moving square on a computer screen and press a button after a beep sound. The button press resulted in an abrupt jump of the moving square on the screen. Experiencing the various stochastic time intervals between the action execution (button-press) and the consequent event (square jumping) caused gradual changes in the subjects' degree of their SoA. By comparing the above theoretical hypothesis with the experimental results, we concluded that the update (adaptation) rule of the prediction model based on the SoA is better described by a Bayesian update than by a stochastic gradient descent.

Tests with binary outcomes (e.g., positive versus negative) to indicate a binary state of nature (e.g., disease agent present versus absent) are common. These tests are rarely perfect: chances of a false positive and a false negative always exist. Imperfect results cannot be directly used to infer the true state of the nature; information about the method's uncertainty (i.e., the two error rates and our knowledge of the subject) must be properly accounted for before an imperfect result can be made informative. We discuss statistical methods for incorporating the uncertain information under two scenarios, based on the purpose of conducting a test: inference about the subject under test and inference about the population represented by test subjects. The results are applicable to almost all tests. The importance of properly interpreting results from imperfect tests is universal, although how to handle the uncertainty is inevitably case-specific. The statistical considerations not only will change the way we interpret test results, but also how we plan and carry out tests that are known to be imperfect. Using a numerical example, we illustrate the post-test steps necessary for making the imperfect test results meaningful.

Introduction. The applicability of mathematical scaling and normalization in solving various applied problems is analyzed. The best known formulas often used along the theoretical and experimental studies are considered. The purpose of this work is to identify the properties of mathematical scaling and rationing.Materials and Methods. The errors obtained under using the mathematical scaling and normalization formulas are considered via specific computational examples. Based on a comparative evaluation of the ratio of the degree of magnitude of the initial and resulting values (as well as the ratio of the degree of difference of these values), the correctness of the results obtained which significantly effects the final values is estimated.Research Results. The analysis leads to the conclusion that some known mathematical scaling and normalization formulas possess properties that are ignored in theory and practice.Discussion and Conclusions. The results obtained allow avoiding erroneous decisions caused by the use of invalid scaling and normalization formulas under solving problems in theory and practice of economics, administrative management, medicine, and plenty of other fields.

Summary: The rapidly growing amount and diversity of data are confronting us more than ever with the need to make informed predictions under uncertainty. The adverse impacts of climate change and natural hazards also motivate our search for reliable predictions. The range of statistical techniques that geomorphologists use to tackle this challenge has been growing, but rarely involves Bayesian methods. Instead, many geomorphic models rely on estimated averages that largely miss out on the variability of form and process. Yet seemingly fixed estimates of channel heads, sediment rating curves or glacier equilibrium lines, for example, are all prone to uncertainties. Neighbouring scientific disciplines such as physics, hydrology or ecology have readily embraced Bayesian methods to fully capture and better explain such uncertainties, as the necessary computational tools have advanced greatly. The aim of this article is to introduce the Bayesian toolkit to scientists concerned with Earth surface processes and landforms, and to show how geomorphic models might benefit from probabilistic concepts. I briefly review the use of Bayesian reasoning in geomorphology, and outline the corresponding variants of regression and classification in several worked examples. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd [ABSTRACT FROM AUTHOR]

We extend the solution concept of perfect Bayesian equilibrium to general games that allow a continuum of types and strategies. In finite games, a perfect Bayesian equilibrium is weakly consistent and a subgame perfect Nash equilibrium. In general games, however, it might not satisfy these criteria. To solve this problem, we revise the definition of perfect Bayesian equilibrium by replacing Bayes' rule with regular conditional probability. The revised solution concept is referred to as perfect regular equilibrium. We present the conditions that ensure the existence of this equilibrium. Then we show that every perfect regular equilibrium is always weakly consistent and a subgame perfect Nash equilibrium, and is equivalent to a simple version of perfect Bayesian equilibrium in a finite game. [ABSTRACT FROM AUTHOR]

A sense of agency (SoA) is the experience of subjective awareness regarding the control of one's actions. Humans have a natural tendency to generate prediction models of the environment and adapt their models according to changes in the environment. The SoA is associated with the degree of the adaptation of the prediction models, e.g., insufficient adaptation causes low predictability and lowers the SoA over the environment. Thus, identifying the mechanisms behind the adaptation process of a prediction model related to the SoA is essential for understanding the generative process of the SoA. In the first half of the current study, we constructed a mathematical model in which the SoA represents a likelihood value for a given observation (sensory feedback) in a prediction model of the environment and in which the prediction model is updated according to the likelihood value. From our mathematical model, we theoretically derived a testable hypothesis that the prediction model is updated according to a Bayesian rule or a stochastic gradient. In the second half of our study, we focused on the experimental examination of this hypothesis. In our experiment, human subjects were repeatedly asked to observe a moving square on a computer screen and press a button after a beep sound. The button press resulted in an abrupt jump of the moving square on the screen. Experiencing the various stochastic time intervals between the action execution (button-press) and the consequent event (square jumping) caused gradual changes in the subjects' degree of their SoA. By comparing the above theoretical hypothesis with the experimental results, we concluded that the update (adaptation) rule of the prediction model based on the SoA is better described by a Bayesian update than by a stochastic gradient descent. [ABSTRACT FROM AUTHOR]

• This study explores the multi-dimensional features of the round-trip carsharing usage pattern. • Stop time thresholds are determined by considering different rental time. • The impact of price incentives on carsharing usage is discussed. Users' usage of carsharing and parking spaces has obvious peak hours. • Hotspots in the spatial distribution of activities are identified. In recent years, the concept of carsharing is rapidly gaining popularity in China, and the round-trip carsharing has become a common mode. However, few studies have revealed the role of round-trip carsharing in users' travel. In this study, the round-trip GPS data provided by a carsharing company in Beijing, China is used to analyze the users' usage patterns based on their trip chains. Through the extraction and analysis of trip information, all trip chains are grouped into three clusters, each of which has a different usage pattern. Then the consumption features and the shared car pick-up and return time of these three patterns are discussed. Further, the Bayes' rule is used to predict the activity purpose, and the proportion and spatial distribution of different purposes are analyzed. Results reveal that the carsharing program presents multiple usage patterns to meet the different travel needs of users. Price incentives like coupons, discounts, and packages can attract more shared car trips. Users' demand for price incentives increases with longer travel distance and time. Also, users' usage of vehicles and parking spaces has obvious peak hours. The spatial distribution of user activities has distinctly different hotspots. This paper can be beneficial for operators to set a reasonable pricing plan and provide better services. [ABSTRACT FROM AUTHOR]

Spark timing, one of the essential parameters to control combustion in spark-ignition gasoline engines, is often advanced to optimize the power output and fuel economy. An overly advanced spark timing, or equivalently a large spark advance, however, can lead to severe knocking under heavy load engine operating conditions. In a trade-off between engine damage avoidance and power enhancement, the knock probability has to be regulated at a low percentage. Based on the observation that the logarithm of the knock intensity under steady operating conditions follows a normal distribution, in this research, a Bayesian knock probability estimation method is proposed using the normal-gamma distribution and the observed knock intensity. Based on the estimation, a spark advance control algorithm is also developed. The proposed knock probability control algorithm is validated on a full-scale test bench with a production spark-ignition engine. The results show that the proposed method is capable of regulating the knock probability to be close to the target percentage. With different parameter settings, the controller can further be configured to behave more aggressively or conservatively in knock probability estimation and regulation. In comparison with the conventional controller and the maximum likelihood–based controller, and in the tip-in/tip-out test, the proposed method also presents a quick response to transient engine operating conditions and a low spark advance dispersion after the spark advance converges close to the borderline. [ABSTRACT FROM AUTHOR]

As an emerging dynamic modeling method that incorporates time-dependent heterogeneity, hidden Markov models (HMM) are receiving increased research attention with regards to travel behavior modeling and travel demand forecasting. This paper focuses on the model transferability of HMM. Based on a series of transferability and goodness-of-fit measures, it finds that HMMs have a superior performance in predicting future transportation mode choice, compared to conventional choice models. Aimed at further enhancing its transferability, this paper proposes a Bayesian conditional recalibration approach that maps the model prediction directly to the context data. Compared to traditional model transferring methods, the proposed approach does not assume fixed parameterization and recalibrates the utilities and the prediction directly. A comparison between the proposed approach and the traditional transfer-scaling favors our approach, with higher goodness-of-fit. This paper fills the gap in understanding the transferability of HMM and proposes a practical method that enables potential applications of HMM. [ABSTRACT FROM AUTHOR]

In absence of randomized‐controlled experiments, identification is often aimed via instrumental variable (IV) strategies, typically two‐stage least squares estimations. According to Bayes' rule, however, under a low ex ante probability that a hypothesis is true (e.g. that an excluded instrument is partially correlated with an endogenous regressor), the interpretation of the estimation results may be fundamentally flawed. This paper argues that rigorous theoretical reasoning is key to design credible identification strategies, the foremost, finding candidates for valid instruments. We discuss prominent IV analyses from the macro‐development literature to illustrate the potential benefit of structurally derived IV approaches. [ABSTRACT FROM AUTHOR]

The general lines of Bayesian modeling (BM) in the study of perception are outlined here. The main thesis argued here is that BM works well only in the so-called secondary processes of perception, and in particular in cases of imperfect discriminability between stimuli, or when a judgment is required, or in cases of multistability. In cases of "primary processes," on the other hand, it is often arbitrary and anyway superfluous, as with the laws of Gestalt. However, it is pointed out that in these latter cases, simpler and more well-established methodologies already exist, such as signal detection theory and individual choice theory. The frequent recourse to arbitrary values of a priori probabilities is also open to question. [ABSTRACT FROM AUTHOR]