Bayesian Theory Research Articles

Research on cross regional green certificate bilateral distributed robust trading strategy based on Bayesian theory

Research on cross regional green certificate bilateral distributed robust trading strategy based on Bayesian theory

Some Properties of Lognormal Uncertain Variables

Uncertainty theory, as a mathematical tool to rationally handle degrees of belief of human beings, has been widely applied in science and engineering. As a basic component of uncertainty theory, uncertainty distribution is a carrier to describe the characterization of an uncertain variable. Moreover, inverse uncertainty distribution provides an easy way to calculate the functions of uncertain variables as well as their expected value. This paper mainly provides the formulas to calculate the expected value, variance, moments and entropy of lognormal uncertain variable. Besides, some operational laws of lognormal uncertain variables are proved.

Gender differences in reported potentially morally injurious events among post-9/11 U.S. combat veterans, using two measures.

Military experiences that violate one's sense of right and wrong (i.e., potentially morally injurious events [PMIEs]) may result in moral injury, characterized by shame, guilt, demoralization, self-condemnation, and social withdrawal. The objective of this study was to examine gender-related differences in the rate of reporting PMIEs. Secondary analyses were conducted on a previously collected longitudinal cohort study of postdeployment functioning among U.S. post-9/11 combat veterans in Central Texas (N = 406). As part of the parent study, participants completed two measures of PMIEs-the Moral Injury Events Scale (MIES; Nash et al., 2013) and the Moral Injury Questionnaire-Military Version (MIQ-M; Currier et al., 2015). Gender differences at the item response level were analyzed in a series of Bayesian multilevel item response theory models. The most frequently endorsed responses on both the MIES and MIQ-M for both men and women were never or strongly disagree. On the MIQ-M, more veteran men endorsed experiencing PMIEs to some degree. On the MIES, more veteran women endorsed strongly agree to betrayal items. Veteran men and women reported any PMIE occurring at relatively high rates. Gender differences at the item response level were found for most items on both the MIES and MIQ-M, albeit of small magnitude. Awareness of gender differences in the likelihood of experiencing a given PMIE may help inform clinical assessments and case conceptualizations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Visual simulation and subjective probability estimation: When seeing is believing.

Visual simulation and subjective probability estimation: When seeing is believing.

Revisiting Inductive Confirmation in Science: A Puzzle and a Solution

When an empirical prediction E of hypothesis H is observed to be true, such observation is said to confirm, i.e., support (although not prove) the truth of the hypothesis. But why? What justifies the claim that such evidence supports the hypothesis? The widely accepted answer is that it is justified by induction. More specifically, it is commonly held that the following argument, (1) If H then E; (2) E; (3) Therefore, (probably) H (here referred to as ‘hypothetico-deductive confirmation argument’), is inductively strong. Yet this argument looks nothing like an inductive generalization, i.e., it does not seem inductive in the term’s traditional, enumerative sense. If anything, it has the form of the fallacy of affirming the consequent. This paper aims to solve this puzzle. True, in recent decades, ‘induction’ has been sometimes used more broadly to encompass any non-deductive, i.e., ampliative, argument. Applying Bayesian confirmation theory has famously demonstrated that hypothetico-deductive confirmation is indeed inductive in this broader, ampliative sense. Nonetheless, it will be argued here that, despite appearance, hypothetico-deductive confirmation can also be recast as enumerative induction. Hence, by being enumeratively inductive, the scientific method of hypothetico-deductive confirmation is justified through this traditional, more restrictive type of induction rather than merely by ampliative induction.

Disagreement Predicts Startup Success: Evidence from Venture Competitions

This paper examines the relationship between disagreement surrounding a startup proposition and its future success. I find that the more venture competition judges disagree on the quality of a startup, the more likely the startup is to succeed, particularly when its proposition is unique. To explain this correlation, I build on the notion that (i) entrepreneurs pursue opportunities based on their subjective beliefs and (ii) common opinion alone cannot be a source of competitive advantage. Therefore, value is disproportionately created and captured by founders with unconventional ideas that spark disagreement, and potential investors should harness disagreement as a predictor of success. I leverage data from 67 venture competitions to show that the empirical implications of this theoretical framework are supported by the data, whereas alternative explanations (e.g., that judges disagree more about risky ventures) are not. Additionally, I provide insights into which evaluators tend to disagree more often (e.g., former entrepreneurs) and which aspects of a startup (e.g., business model) are most polarizing. This work has broad implications for investors and institutions that strive to evaluate the potential of startup ideas. Supplemental Material: The online appendix is available at https://doi.org/10.1287/stsc.2024.0169 .

Bayesian deep learning: An enhanced AI framework for legal reasoning alignment

The integration of artificial intelligence into the field of law has penetrated the underlying logic of legal operations. Currently, legal AI systems face difficulties in representing legal knowledge, exhibit insufficient legal reasoning capabilities, have poor explainability, and are inefficient in handling causal inference and uncertainty. In legal practice, various legal reasoning methods (deductive reasoning, inductive reasoning, abductive reasoning, etc.) are often intertwined and used comprehensively. However, the reasoning modes employed by current legal AI systems are inadequate. Identifying AI models that are more suitable for legal reasoning is crucial for advancing the development of legal AI systems.Distinguished from the current high-profile large language models, we believe that Bayesian reasoning is highly compatible with legal reasoning, as it can perferm abductive reasoning, excel at causal inference, and admits the "defeasibility" of reasoning conclusions, which is consistent with the cognitive development pattern of legal professionals from apriori to posteriori. AI models based on Bayesian methods can also become the main technological support for legal AI systems. Bayesian neural networks have advantages in uncertainty modeling, avoiding overfitting, and explainability. Legal AI systems based on Bayesian deep learning frameworks can combine the advantages of deep learning and probabilistic graphical models, facilitating the exchange and supplementation of information between perception tasks and reasoning tasks. In this paper, we take perpetrator prediction systems and legal judegment prediction systems as examples to discuss the construction and basic operation modes of the Bayesian deep learning framework. Bayesian deep learning can enhance reasoning ability, improve the explainability of models, and make the reasoning process more transparent and visualizable. Furthermore, Bayesian deep learning framework is well-suited for human-machine collaborative tasks, enabling the complementary strengths of humans and machines.

Identification of material excavation difficulty and uncertainty analysis based on Bayesian deep learning

Accurately assessing the difficulty of material excavation is crucial for reducing excavator energy consumption, ensuring operational safety, and optimizing excavator efficiency. Addressing the challenges of uncertain and difficult-to-judge excavation conditions for underground materials, this paper proposes a Bayesian deep learning-based method that integrates excavation process data to identify excavation difficulty. Firstly, we constructed a deep learning model based on Bayesian theory and decomposed the uncertainty of the identification results into aleatory uncertainty and epistemic uncertainty. Next, through a mechanistic analysis of the interaction between materials and the excavator bucket during excavation, we identified the input features for the model. Finally, we validated the effectiveness of the method through experiments. The results show that the proposed method not only accurately identifies the excavation difficulty of the material but also quantifies and decomposes the uncertainty of the identification results, demonstrating both theoretical significance and practical application value.

Who am I? A second-order meta-analytic review of correlates of the self in childhood and adolescence.

People's subjective beliefs about themselves affect what people think and, consequently, what they do. Positive self-beliefs are important for many life outcomes, from academic success to well-being, especially during K-12 education as a crucial developmental period. Many empirical studies and meta-analyses have examined correlates of self-beliefs. The present second-order meta-analytic review integrates this large and diverse body of research, addressing two research aims: First, we examined the comparative strength of different variables related to self-beliefs. Second, we provide a methodological review of meta-analyses in this area, thereby facilitating readers' ability to assess the risk of bias when interpreting the results. We summarized 105 first-order meta-analyses published before July 2023 that investigated variables associated with self-beliefs during K-12 education, comprising 493 first-order effect sizes based on more than 8,500 primary studies and more than 16 million children and adolescents. We computed second-order standardized mean differences (SMD) using two-level meta-analyses with robust variance estimation. Personal characteristics (SMD = 0.50) showed stronger relations with self-beliefs than interventions (SMD = 0.27). Achievement (SMD = 0.66) and noncognitive variables (SMD = 0.67) were the personal characteristics most strongly related to self-beliefs compared to cognitive abilities (SMD = 0.30) and background variables (SMD = 0.21). Interventions targeting individual characteristics (SMD = 0.35) and especially self-beliefs (SMD = 0.52) showed larger effect sizes than interventions that focused on improving teaching and classroom structure (SMD = 0.20). Few meta-analyses investigated situational aspects, such as the geographical origin of the sample, in association with children's and adolescents' self-beliefs. Overall, this second-order meta-analytic review provides a comprehensive map of correlates of the self, highlighting pathways for future research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Integrating Factor Models Using Bayesian Model Averaging: Evidence from Korea

This study applies the Bayesian model averaging approach to the Korean stock market data to yield an integrated factor model that can address model uncertainty. During the period from 2004 to 2022, we choose 14 factors and 10 macro predictors and generate a total of 10,485,760 candidate factor models by considering all possible combinations of each variable and whether to allow for model mispricing. The integrated model estimates the expected return and covariance matrix by combining the predictions of all the candidate models using Bayesian posterior probabilities as weights. In the Korean stock market, there is no clear winner model with dominant posterior probabilities, implying that model uncertainty is substantial. The optimal portfolio of the integrated model has a higher out-of-sample Sharpe ratio and lower downside risk than benchmark factor models. Model uncertainty has a significant impact on the estimation of the covariance matrix of the integrated model, and model disagreement about expected returns is particularly acute during periods of sharp market declines. In the presence of model uncertainty, Bayesian investors perceive equities to be riskier than historical volatility.

Automatic Method for Detecting Deformation Cracks in Landslides Based on Multidimensional Information Fusion

As cracks are a precursor landslide deformation feature, they can provide forecasting information that is useful for the early identification of landslides and determining motion instability characteristics. However, it is difficult to solve the size effect and noise-filtering problems associated with the currently available automatic crack detection methods under complex conditions using single remote sensing data sources. This article uses multidimensional target scene images obtained by UAV photogrammetry as the data source. Firstly, under the premise of fully considering the multidimensional image characteristics of different crack types, this article accomplishes the initial identification of landslide cracks by using six algorithm models with indicators including the roughness, slope, eigenvalue rate of the point cloud and pixel gradient, gray value, and RGB value of the images. Secondly, the initial extraction results are processed through a morphological repair task using three filtering algorithms (calculating the crack orientation, length, and frequency) to address background noise. Finally, this article proposes a multi-dimensional information fusion method, the Bayesian probability of minimum risk methods, to fuse the identification results derived from different models at the decision level. The results show that the six tested algorithm models can be used to effectively extract landslide cracks, providing Area Under the Curve (AUC) values between 0.6 and 0.85. After the repairing and filtering steps, the proposed method removes complex noise and minimizes the loss of real cracks, thus increasing the accuracy of each model by 7.5–55.3%. Multidimensional data fusion methods solve issues associated with the spatial scale effect during crack identification, and the F-score of the fusion model is 0.901.

Incorporating costs and benefits to the evaluation of uncertain research results: Applications to cancer research funding

Abstract Correctness is a key aspiration of the scientific process, yet recent studies suggest that many high-profile findings may be difficult to replicate or require considerable evidence for verification. Proposals to fix these issues typically ask for tighter statistical controls (e.g., stricter p-value thresholds or higher statistical power). However, these approaches often overlook the importance of contemplating research outcomes’ potential costs and benefits. Here, we develop a framework grounded in Bayesian decision theory that seamlessly integrates cost-benefit analysis into evaluating research programs with potentially uncertain results. We derive minimally acceptable pre-study odds and positive predictive values for cost and benefit levels. We show that tolerance to inaccurate results changes dramatically due to uncertainties posed by research. We also show that reducing uncertainties (e.g., by recruiting more subjects) may have limited effects on the expected benefit of continuing specific research programs. We apply our framework to several types of cancer research and their funding. Our analysis shows that highly exploratory research designs are easily justifiable due to their potential benefits, even when probabilistic models suggest otherwise. We discuss how the cost and benefit of research could and should always be part of the toolkit used by scientists, institutions, or funding agencies.

A human factor analysis of remote-control failure for maritime autonomous surface ships

Human factor management is crucial to the safety of maritime shipping. Whilst there has been extensive research on human factors in conventional shipping, the development of autonomous shipping necessitates the investigation of emerging human factors under the evolving landscape. Given that the research on human factor analysis for remote operation failure is limited, this study aims to enrich the literature by establishing a comprehensive human factor framework and empirically assessing the factors. Based on the literature review and expert interview, a total of ten categories and thirty-one human factors are organized under the human factor analysis and classification system. Adopting the purposive sampling, questionnaire surveys are then distributed to experts specializing in ship operations and automation to collect their degree of belief on potential human factors. A hybrid method composed of evidential reasoning and the rule-based Bayesian network is applied to analyze the data. The empirical results indicate that adverse mental states and adverse cognitive states are perceived to be the most threatening risk categories in remote operations. This study expands the academic research on human factor analysis and highlights the significance of managing psychological and cognitive factors. The findings provide managerial implications for the future safety management of autonomous ships.

Probability Updating in the Classical and the Quantum Model

Imagine a Bayesian decision agent who is keen to invest only in technologies or businesses that are conductive to achieving a sustainable economic future. Being initially keen to invest in a certain technology, subsequently two pieces of new information are received that both individually reduce the agent’s inclination to make that investment. In such a situation, it would be natural to assume that the simultaneous consideration of both pieces of information should further reduce the agent’s initial enthusiasm; after all the Bayesian method has been called “ nothing but common sense reduced to calculation.” Somewhat surprisingly, making general statements like this about the double conditional probability requires substantial additional assumptions in classical Bayesian probability theory. We investigate four schemes of assumptions that allow ‘reasonable’ conclusions about the double conditional probability. We compare this with two schemes for the quantum probability model where the double conditional results from sequential updating through projections.

Prospective comparison of temporal myocardial function in men vs women after anterior ST-elevation myocardial infarction

Abstract Background Compared to men, women have been reported to have increased morbidity and mortality after ST-elevation myocardial infarction (STEMI), but sex differences in cardiac function in the acute and subacute phases of STEMI are incompletely understood. Objective To prospectively compare changes in cardiac function over the acute and subacute phases after anterior STEMI with timely reperfusion in women versus men. Methods The Stunning in Takotsubo versus Acute Myocardial Infarction (STAMI) study prospectively enrolled 94 men and 36 women with anterior STEMI. Echocardiography and blood sampling were performed within 4 hours of admission and at 1, 2, 3, 7, 14 and 30 days after admission. The primary outcome was akinesia recovery, defined as the difference in the per cent akinesia observed at baseline versus follow-up. Secondary outcomes included wall motion score index (WMSI), left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS). Mixed effects linear regression or zero-inflated tobit models with random intercepts were used to model echocardiographic parameters over time. Results Baseline patient characteristics were similar in both groups. The difference between women and men in akinesia recovery at 30 days was 8.4% (95% confidence interval [CI] 1.23-14.99). The covariate-adjusted Bayesian posterior probability that akinesia recovery and WMSI improvement at 30 days are more significant in women than men were 95% and 99%, respectively. Similar but less pronounced trends towards greater improvement in women than men were observed for LVEF and GLS. Conclusions After anterior STEMI with timely reperfusion, cardiac dysfunction recovered to a greater extent in women than in men.

Linking quantum discord with Bayesian game theory

Linking quantum discord with Bayesian game theory

Multiple Maternal Lineages of <i>Strauchbufo raddei</i> (Strauch, 1876) in Mongolia Revealed by Mitochondrial DNA

The Mongolian toad Strauchbufo raddei (Strauch, 1876) is one of widely distributed and dominated amphibian species in eastern Asian, including Mongolia. In this study, we amplified and sequenced a total of 50 samples of S. raddei, from three main regions in its Mongolian distribution range namely, Northern, Eastern, and Gobi regions. Our phylogenetic trees (Bayesian inference; BI and time-calibrated) and network analysis clearly support presence of multiple maternal mitochondrial DNA lineages for S. raddei in Mongolia. As expected, samples from these three regions were clustered separately in BI tree (Bayesian posterior probability (BPP 0.52). In addition, the population specific haplotypes, estimated genetic distances (e.g. uncorrelated p distance; ranged from 1% to 25%) and hierarchical AMOVA suggested relatively strong population genetic structure of S. raddei across Mongolia. Time-calibrated tree and ancestral range analysis showed that the first colonization of S. raddei may have occurred in Northern part of Mongolia approximately 9.63 mya, then dispersed over other parts of the country. Estimated approximate time for crossing event of S. raddei over dry steppe and semi-desert regions in Mongolia was 5.02 – 2.50 mya. Finally, high level of genetic diversity (overall; haplotype diversity, Hd = 0.93; nucleotide diversity, π = 0.011) of S. raddei were observed in Mongolia. Importantly, through this study, we contributed to closing sampling gaps in distribution range of S. raddei in Eastern Asia, which is key to better understanding species history, as well as describing cryptic lineages.

Transferability of real-time EVT safety models: an investigation using autonomous vehicles data

Recent research has shown that data from autonomous vehicles (AVs) enables proactive, real-time road safety management. However, with low AV market penetration, it's crucial to assess the transferability of AV-based safety models to other locations. This study transfers real-time safety models from a data-rich to a data-scarce environment using vehicular conflict data collected by AVs. Multi-site Bayesian hierarchical Extreme Value Theory models are developed in two cities, with model transfer evaluated using informative priors and parameter recalibration. Both approaches produce reliable results, though the transferability index favors the informed prior method, while predictive deviance shows similar performance for both. Model sensitivity to temporal data limitations is assessed, revealing that the recalibration approach is more sensitive to block size, whereas the informed prior method remains stable. A novel method mimicking data limitations shows transferred models perform well with data utilizations above 20%, promoting transferability to mitigate data inadequacy.

A game-based decision-making method for multi-ship collaborative collision avoidance reflecting risk attitudes in open waters

To accurately reflect risk attitudes towards ship intentions in multi-ship encounters, this paper develops a novel two-stage collaborative collision avoidance decision-making (CADM) model by incorporating intention prediction and real-time decision-making. We acquire prior knowledge of risk attitudes by analyzing Automatic Identification System (AIS) data and further estimate the probability distributions of encountering ship's risk attitude using Bayesian reasoning. By treating collision avoidance procedure as a static game with incomplete information, a predictive model for collision avoidance intentions is developed by taking account into risk attitude probabilities. Real-time decisions are then implemented according to different stages, and a collaborative CADM model is established by a game-decision cycle. Finally, a multi-ship encounter scenario is simulated under all combinations of risk attitudes, and the results are compared with those obtained under complete information. The results demonstrate that the proposed model can formulate avoidance actions that meet safety requirements under all combinations of risk attitudes. Further comparison with complete information proves the effectiveness of the risk attitude probability model, which is conducive to improving the decision-making flexibility and reducing complexity. The research findings enhance the collaborative decision-making, contributing to the development of autonomous navigation in open waters.

A comparison of imprecise Bayesianism and Dempster–Shafer theory for automated decisions under ambiguity

Abstract Ambiguity occurs insofar as a reasoner lacks information about the relevant physical probabilities. There are objections to the application of standard Bayesian inductive logic and decision theory in contexts of significant ambiguity. A variety of alternative frameworks for reasoning under ambiguity have been proposed. Two of the most prominent are Imprecise Bayesianism and Dempster–Shafer theory. We compare these inductive logics with respect to the Ambiguity Dilemma, which is a problem that has been raised for Imprecise Bayesianism. We develop an agent-based model comparison that isolates the difference between the two inductive logics in their updating methods. We find that Dempster–Shafer theory does not avoid the Ambiguity Dilemma. We discuss the implications of this result.