Bayesian Theory Research Articles

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.

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.

The Development and Piloting of an Early Youth-Engagement (EYE) Model to Improve Engagement of Young People in First Episode Psychosis Services: A Mixed Methods Study.

Psychosis is associated with significant health and societal costs. Early intervention in psychosis services (EIP) are highly effective in promoting recovery, yet substantial proportions of young people disengage. The current study aimed to develop and evaluate a novel engagement intervention in EIP services. A qualitative investigation of facilitators and barriers to engagement in 68 first episode psychosis patients, family members and young people, and a Delphi consultation with 27 regional and national youth and psychosis service leads informed the development of the intervention. A mixed-methods feasibility-pilot study then compared engagement outcomes in 298 EIP service users in two cohorts: standard EIP versus standard EIP plus the novel early youth-engagement (EYE) intervention. A qualitative study explored intervention experiences in 22 randomly selected service users, carers and clinicians. A process evaluation explored delivery. Disengagement was 24% in the standard EIP cohort compared to 14.5% in the standard EIP plus EYE intervention cohort. A 95% Bayesian credibility interval revealed a 95% probability that the true reduction in disengagement lay somewhere between 0% and 18%. The number needed to treat was 11, 95% CI [5, 242]. Use of the EYE resources was associated with engagement. Qualitiative feedback supported effects on communication, social network engagement, service user goals, mental health and well-being outcomes. The EYE intervention was designed from a service user, young person and carer perspective. Both qualitative and quantitative data support impacts on engagement. We now need to evaluate effectiveness, cost-effectiveness and implementation in a multi-site randomised controlled trial.

OS09.7.A GBM AGILE PLATFORM TRIAL FOR NEWLY DIAGNOSED AND RECURRENT GBM: RESULTS OF FIRST EXPERIMENTAL ARM, REGORAFENIB

Abstract BACKGROUND GBM AGILE (NCT03970447;https://www.gcaresearch.org/research/gbm-agile) is a phase 3 Bayesian adaptive platform trial that efficiently tests multiple arms against common control, with 6 arms included to date. Primary endpoint is overall survival (OS). Stage 1 experimental arms are adaptively randomized against other arms. Demonstrated efficacy in stage 1 leads to fixed randomization stage 2. Stages 1 and 2 are combined for registration. Control randomization is fixed. Regorafenib, a multikinase-inhibitor, entered into GBM AGILE as the first arm and therefore was equally randomized against control. Regorafenib showed OS benefit in recurrent disease (RD) in randomized phase 2 REGOMA trial. MATERIAL AND METHODS Patient subtypes in GBM AGILE are newly diagnosed unmethylated (NDU), RD, and—not considered for regorafenib—ND methylated (NDM). Arm indications (signatures) are combinations of subtypes. Control is temozolomide (ND) and lomustine (RD). Efficacy is assessed by OS hazard ratio(HR), arm/control. Efficacy is demonstrated when Bayesian probability of benefit (HR< 1.00) ≥ 98% (roughly analogous P-value: 0.02). Futility occurs at any monthly analysis when Bayesian predictive power (PP) is < 25% for all signatures. Follow-up continues for 12 months after arm’s accrual stops. RESULTS When PP for all 3 pre-defined signatures was < 25%, regorafenib’s accrual was stopped for futility. Regorafenib/control sample sizes were 49/51, 127/128, 176/179 for signatures NDU, RD, and both. Respective PPs: 0.138, 0.030, 0.025—none close to 0.25. Respective mean HRs: 1.26, 1.25, 1.23. Probabilities of benefit (HR< 1.00): 0.35, 0.18, 0.17. At final analysis, mean HRs were 1.07, 1.08, 1.08 with final probabilities of benefit (HR< 1.00) equal to 0.421, 0.312, 0.296—none close to 0.98. CONCLUSION GBM AGILE efficiently and compellingly addressed regorafenib’s role in GBM, in RD and NDU. These findings are germane as they fail to confirm the REGOMA results in RD. GBM AGILE continues to efficiently assess other therapies, including utilizing concurrent and previously accrued controls. This abstract was accepted and previously presented at the 2023 SNO Annual Meeting and published in Neuro-Oncology, Volume 25, Issue Supplement_5, November 2023, Pages v97-v98.

An algorithm for belief rule induction with partial ignorance

Belief rules are an extension of fuzzy rules that consider belief degrees. They are widely applied due to their traceability, interpretability, and flexibility. However, current belief rules only consider residual support and global ignorance while ignoring partial ignorance. In this paper, we proposed a novel belief rule-based classification system called partial ignorance belief rule induction algorithm (PIBRIA). The consequent part of belief rules takes into account not only the residual support and global ignorance but also partial ignorance of evidence. The belief degrees in the consequent part of each belief rule collectively form a genuine basic probability assignment (BPA). Based on fuzzy unordered rule induction algorithm (FURIA), we propose the learning algorithm for belief rules with partial ignorance, which helps the novel proposed PRIBIA achieve a balance between accuracy and complexity. The inference method employs the Dempster’s combination rule and Shafer’s discounting operation, where the discounting operation can resolve evidence conflicts in the combination rule. The effectiveness and superiority of PIBRIA in handling classification tasks have been validated by comparing it to some state-of-the-art rule-based models. The results of statistical tests show that PIBRIA outperforms all other methods in terms of classification accuracy. Whereas its computational complexity and model complexity are moderate.

Penalized Bayesian D-Optimal Designs for Regression Models of Continuous Response

We propose extending the use of desirability functions in Bayesian optimal designs for regression models. This technique generates experimental designs with good statistical inference properties according to Bayesian optimal design theory and practical features, as defined by an investigator. These practical features are defined by a penalty function, using an overall desirability function, which is added to a Bayesian $D$-optimal design criterion to penalize impractical experimental designs. This methodology is illustrated by two examples of regression models: quadratic and exponential. Then, we compare designs obtained for different prior distributions of unknown parameters by efficiency calculations and simulation study. Results show that the $D$-efficiencies of the penalized designs relative to the non-penalized Bayesian D-optimal designs are competitive.

Leaf Rust Pathogens on Hypericum pseudohenryi: Describing Melampsora danbaensis sp. nov. and M. hyperici-pseudohenryi sp. nov. from China.

Based on morphological and phylogenetic evidence, two novel species of Melampsora were discovered on Hypericum pseudohenryi in China and have been thoroughly characterized. One of these species, designated as M. danbaensis, exhibits distinct features such as aecia of Uredo-type, typically appearing in gregarious or grouped arrangements, and presenting a shallowly pulvinate structure. Aeciospores exhibit tremendous variations in size, ranging in shape from globose to ellipsoidal and bearing pronounced verrucose texture. Telia resemble crusts one-spore deep, covering nearly the entire abaxial leaf surface, with sessile teliospores reaching sizes of up to 65.8 µm, and exhibiting a clavate to cylindrical shape. Another species, designated as M. hyperici-pseudohenryi, is distinguished by Uredo-type uredinia, which are hypophyllous, scattered or grouped, and interspersed with numerous paraphyses. Its urediniospores tend to be globose, ellipsoidal or obovoid, echinulate, and are accompanied by clavate to capitate paraphyses reaching lengths up to 77.6 µm. Phylogenetically, both species form a novel monophyletic clade within the Melampsora genus, with robust support demonstrated by a high Maximum likelihood bootstrap support (MLBS) value of 100% and a Bayesian posterior probability (BPP) of 1. This study enriches our understanding of the diversity and geographical distribution of Melampsora species that infect Hypericum plants in China.

Cyanobacterial blooms prediction in China’s large hypereutrophic lakes based on MODIS observations and Bayesian theory

Cyanobacterial harmful algal blooms (HABs) pose a significant threat to aquatic ecosystems, water quality, and public health, particularly in large hypereutrophic lakes. Developing accurate short-term prediction models is essential for early warning and effective management of HABs. This study introduces a Bayesian-based model aimed at predicting HABs in three of China’s large hypereutrophic lakes: Lake Taihu, Lake Chaohu, and Lake Hulunhu. By integrating MODIS data from the Terra and Aqua satellites with meteorological data spanning from 2010 to 2018, the model forecasts HABs distributions 1, 4, and 7 days in advance. Validation with meteorological data from 2019 to 2020 showed high accuracy, with 0.83 at the pixel level, 0.74 for zonal predictions, and 0.64 for lake-wide HABs area forecasts. Further evaluation using 2023 weather forecast data yielded similar accuracies of 0.78, 0.57, and 0.62, respectively. In addition to predicting the spatial extent of HABs, the model provides binary HABs maps, outbreak areas, and HABs status within lake zones. This method for building prediction models significantly enhances early warning and management capabilities for HABs, providing a scalable framework that can be adapted to other regions facing similar threats from HABs.

Late acute stress effects on decision-making: The magnified attraction to immediate gains in the iowa gambling task

Previous literature indicates that the later phases of the acute stress response may promote poor decision-making, characterized by riskier choices and a likely inclination towards immediate reward-seeking. However, all studies addressing the effect of this phase have treated decisional capacity as a singular dimension, without analyzing the underlying processes under decision-making. Employing the Value-Plus-Perseveration (VPP) RL model, based on Bayesian logic, this study aims to gain specific insights into how late phase of acute stress impacts the cognitive processes underpinning decision-making in the Iowa Gambling Task (IGT), deciphering whether, as expected, gains are processed in a magnified manner. Seventy-three participants were randomly assigned to two groups, stress (N = 35) and control (N = 38). A virtual version of The Trier Social Stress Test (TSST-VR) was employed as a laboratory stressor. Decision-making was evaluated 35 minutes after the stressor onset, by means of the IGT. Results showed that stressed participants, in comparison to control group, displayed more perseverant and consistent decision-making, enhanced memory, and reinforcement learning capabilities, yet were guided by a greater attraction to decks offering immediate high gains. These results are analyzed with the understanding that in the IGT, short-term decisions focused on instant rewards are seen as counterproductive. This suggests that stress could limit the ability to switch to strategies that are more cautious and offer greater long-term benefits.

Fuzzy-Bayesian Expert System for Suggesting Personalized Training Plans with Exercises and Routines with a Model Driven Architecture (MDA)

In the dynamic world of fitness, the quest for tailor-made exercise plans has never been more critical. Expert systems are stepping up to this challenge, offering a promise of personalized routines for both seasoned athletes and newcomers. In this article, we introduce an ingenious fuzzy-Bayesian expert system, armed with fuzzy and Bayesian logic, that tackles the uncertainties lurking within fitness data. It's a system with a dual focus: for fitness veterans, it conducts a meticulous analysis of their training history, past achievements, and current ambitions. Guided by the nuanced power of fuzzy and Bayesian logic, it makes decisions rooted in probabilities. As for beginners, the system builds a strong foundation, considering your present fitness status and your future aspirations. This forward-thinking approach speaks directly to the increasing demand for adaptable fitness solutions. For seasoned athletes, it paves the way to peak performance and injury prevention. Meanwhile, beginners embark on their fitness journey with gradual and secure steps. The Fuzzy-Bayesian Expert System offers a comprehensive answer that embraces data diversity and individual fitness aspirations in both scenarios. Furthermore, the seamless integration of the UPAFuzzySystem (Montes Rivera et al., 2023) library and Twilio elevates the system's functionality and its connection with users, turning it into a personal fitness enhancement tool. Dive into a world where fitness meets technology for a custom-made future of health and well-being.

Adaptation and validation of an inventory for assessing subjective task value beliefs regarding the decision to pursue postgraduate studies

The desire to pursue postgraduate studies is motivated by several interrelated and multifaceted factors ranging from intrinsic to extrinsic. However, little consensus exists in the literature regarding an instrument that assesses an individual’s motivation for pursuing postgraduate education. In this study, we adapted and validated a pre-existing inventory that evaluates the individuals’ motives for pursuing postgraduate studies using the subjective task value beliefs framework. The study, in particular, focused on assessing the factor structure, construct validity, and reliability of the adapted scale. Study participation invitations were sent to a pool of postgraduate students in two higher education institutions, of which 501 students finally responded to the online questionnaire. The exploratory and confirmatory factor analyses were used to analyse the data to address the research objectives. The results revealed a five-factor structure for the adapted scale with excellent construct validity and reliability indicators. The findings of the research add value to the psychometric literature and also provide a useful instrument for use among instructors, researchers, and university administrators in their line of duty.