Bayesian Theory Research Articles

Exploring the dynamic relations between second language students’ classroom engagement and task value belief: A longitudinal study

BackgroundStudent engagement and subjective task value belief are critical psychological constructs driving the second language (L2) learning process. L2 research has established the positive effect of subjective task value belief on student engagement in the L2 classroom, while the reverse effect has received some theoretical and empirical support in the broader field of educational psychology. However, theoretically grounded empirical work on testing the reciprocal relationship between these two constructs remains absent in L2 research. AimThis study sought to examine the longitudinal reciprocal relationship between L2 students’ classroom engagement and their subjective task value belief – the relationship that is neither sufficiently theorized nor empirically tested in the L2 learning context. SampleData were collected across three time points over an academic semester from 920 undergraduate students learning English as a foreign language in Vietnam. MethodRandom intercept cross lagged panel modelling was conducted to examine the carry-over (or autoregressive) effects of L2 students' subjective task value belief (or their classroom engagement) at one time on itself at a subsequent time, as well as the concurrent effects and spill-over (or cross-lagged) effects of L2 students’ subjective task value belief on their classroom engagement and vice versa. ResultL2 students’ classroom engagement and their subjective task value belief not only co-varied within individuals concurrently, but variation in one construct led to subsequent variation in another over the semester. ConclusionFindings confirm the dynamic, situation- and time-specific patterns of relationship between the two constructs in line with the expectancy value theory (Eccles & Wigfield, 2020a) and the development-in-sociocultural context model of student engagement (Wang, Henry, & Degol, 2020).

Prestack reservoir prediction method in ray parameter domain based on wide azimuth ocean bottom node seismic data

AbstractWide azimuth seismic data play an important role in deep reservoir prediction. According to the Paleogene clastic rock reservoir prediction, the high‐density and wide azimuth 3D seismic data acquisition of ocean bottom nodes was first carried out in a Chinese offshore oilfield in 2019. After high precision amplitude‐preserving processing, we obtained the high‐quality wide azimuth gathers. However, the research on anisotropy and reservoir prediction using wide azimuth seismic data mainly focuses on carbonate and bedrock intervals, which is not suitable for clastic rock reservoir prediction. Therefore, this paper innovatively proposes a clastic rock reservoir prediction method, which studies prestack reservoir prediction in the ray parameter domain based on wide azimuth ocean bottom node seismic data. Based on the azimuth gathers, we can obtain elastic parameters through prestack amplitude versus offset inversion, which is used to characterize the reservoir, so it is significant to obtain high precision elastic parameters in order to get highly reliable reservoir prediction results. In this paper, we develop an amplitude versus offset inversion method based on the Bayesian theory in ray parameter domain, the output of which is density, P‐wave impedance and Vp/Vs. These elastic parameters have high precision, and density data are valuable input for reservoir characterization because they are sensitive to the lithology of clastic rock reservoir at different orientations. In ray parameter domain inversion, the ray path of seismic wave propagation is considered polyline, which is more consistent with the actual situation; thus, extracted amplitudes of P gathers used in inversion are more accurate. In addition, the reflection coefficient formula in ray parameter domain has higher precision when the incident angle is large. The inversion based on the Bayesian theory can improve the stability of the inversion. Test on the actual data shows that the result of ray parameter domain inversion with a Bayesian scheme is more accurate, stable and reliable. Based on the above high precision density inversion results, an innovative wide azimuth data reservoir prediction technology based on elliptical short‐axis fitting was proposed. The actual prediction of the deep reservoir in the Bohai oilfield shows that sand thickness fitting prediction results in the short axis can best match the actual drilling sandstone thickness. The coincidence rate is 86% and the short‐axis fitting results are more in agreement with geological laws. Theoretical research and practical applications have shown that this method is feasible and effective, with high prediction accuracy, computational efficiency and strong application value.

A tale of two constructs: combined assessment of demoralization and subjective incompetence

ABSTRACT Demoralization comprises multiple dimensions. Among them, Subjective Incompetence (SI) is the perception of being incapable of appropriate action in demanding circumstances. SI may be an early sign of demoralization preceding hopelessness, thus we aimed at integrating items related to Subjective Incompetence into the clinical assessment of demoralization. We assessed 414 subjects from the general population with the Demoralization Scale, 24 items (DS24) and the Subjective Incompetence Scale, 12 items (SIS12). We used multiple approaches to detect the optimal number of factors and their item structure, then conducted Bayesian Item Response Theory analyses to study item psychometric properties. Item Response Theory models were used to extrapolate latent severity ratings of clinical dimensions. We modelled the DS24 with five factors (Disheartenment, Sense of Failure, Helplessness, Irritability, Loss of Purpose) and the SIS12 with three (Subjective Incompetence, Inability to plan, Inability to Deal). The more complex IRT model had the best predictive value and helped to identify the items with better discrimination properties across the different dimensions. Twenty items were retained and used to develop the combined Demoralization and Subjective Incompetence Scale (DSIS20), which maintained high correlation with raw and latent trait scores of the longer versions. We combined selected items of the DS24 and the SIS12 to develop the DSIS20, a shorter assessment instrument that includes Subjective Incompetence as well as other clinical dimensions of demoralization. Further study may clarify if DSIS20 may be helpful for the early detection of demoralization.

Bridging the gap between subjective probability and probability judgments: The quantum sequential sampler.

Bridging the gap between subjective probability and probability judgments: The quantum sequential sampler.

A probability model for estimating age in young individuals relative to key legal thresholds: 15, 18 or 21-year.

Age estimations are relevant for pre-trial detention, sentencing in criminal cases and as part of the evaluation in asylum processes to protect the rights and privileges of minors. No current method can determine an exact chronological age due to individual variations in biological development. This study seeks to develop a validated statistical model for estimating an age relative to key legal thresholds (15, 18, and 21years) based on a skeletal (CT-clavicle, radiography-hand/wrist or MR-knee) and tooth (radiography-third molar) developmental stages. The whole model is based on 34 scientific studies, divided into examinations of the hand/wrist (15 studies), clavicle (5 studies), distal femur (4 studies), and third molars (10 studies). In total, data from approximately 27,000 individuals have been incorporated and the model has subsequently been validated with data from 5,000 individuals. The core framework of the model is built upon transition analysis and is further developed by a combination of a type of parametric bootstrapping and Bayesian theory. Validation of the model includes testing the models on independent datasets of individuals with known ages and shows a high precision with separate populations aligning closely with the model's predictions. The practical use of the complex statistical model requires a user-friendly tool to provide probabilities together with the margin of error. The assessment based on the model forms the medical component for the overall evaluation of an individual's age.

A grouping module assessment method for ocean engineering systems: Subsea tree system as a case

In the ocean engineering environment, the quality failure data of complex systems are difficult to obtain due to the high experiment cost. In addition, using a single model to analyze risk, reliability, availability, and maintainability is a big challenge. Based on the fault tree, Dynamic Bayes and Markov models, a state assessment method for ocean engineering systems with multiple maintenance modes is proposed in this paper. This method gives full play to the advantages of the three methods, and comprehensively analyzes the risk, reliability, availability and maintainability. This method uses fault tree and Markov model to pre-process fault data, and then inputs the pre-processed fault data into the multi-state degradation model based on dynamic Bayesian theory. Considering the maintenance strategies of no repair, perfect repair, imperfect repair and preventive repair, the model is iterated and adjusted until the model has processed all the event data and the updated model can best reflect the state of the system. The method is verified by taking the subsea tree of the subsea production system as an example. The obtained tree reliability index (mean time without failure) is basically consistent with the failure statistics of offshore and onshore reliability databases, which verifies the accuracy of the proposed method.

Comparing the efficacy of cipaglucosidase alfa plus miglustat with other enzyme replacement therapies for late-onset Pompe disease: a network meta-analysis utilizing patient-level and aggregate data.

Aim: Late-onset Pompe disease is characterized by progressive loss of muscular and respiratory function. Until recently, standard of care was enzyme replacement therapy (ERT) with alglucosidase alfa. Second-generation ERTs avalglucosidase alfa (aval) and cipaglucosidase alfa with miglustat (cipa+mig) are now available. Without head-to-head trials comparing aval with cipa+mig, an indirect treatment comparison is informative and timely for understanding potential clinical differentiation. Materials & methods: A systematic literature review was performed to identify relevant studies on cipa+mig and aval. Using patient-level and aggregate published data from randomized controlled trials (RCTs) and phase I/II and open-label extension (OLE) trials, a multi-level network meta-regression was conducted, adjusting for various baseline covariates, including previous ERT duration, to obtain relative effect estimates on 6-minute walk distance (6MWD, meters [m]) and forced vital capacity (FVC, %predicted [pp]). Analyses of two networks were conducted: Network A, including only RCTs, and network B, additionally including single-arm OLE and phase I/II studies. Results: Network B (full evidence analysis) showed that cipa+mig was associated with a relative increase in 6MWD (mean difference 28.93m, 95% credible interval [8.26-50.11m]; Bayesian probability 99.7%) and FVC (2.88pp [1.07-4.71pp]; >99.9%) compared with aval. The comparison between cipa+mig and aval became more favorable for cipa+mig with increasing previous ERT duration for both end points. Analysis of network A showed that cipa+mig was associated with a relative decrease in 6MWD (-10.02m [-23.62 to4.00m]; 91.8%) and FVC (-1.45pp [-3.01 to0.07pp]; 96.8%) compared with aval. Conclusion: Cipa+mig showed a favorable effect versus aval when all available evidence was used in the analysis.

Personalized assessment of eating disorder cognitions during treatment: A new measure of cognitive pathology change

ObjectiveModifying cognitive distortions, or thinking errors, is crucial in eating disorders (ED) treatment. To address the lack of a personalized measure for ED cognitions, the Thought Inventory was developed. The study aimed to establish its feasibility and validity, identify thinking error contents and types, examine changes in belief of irrational thoughts, and investigate associations with change in ED symptoms. Hypotheses, procedure, and planned analyses were pre-registered to ensure transparency. MethodsParticipants (N = 55) completed the Thought Inventory, the Eating Disorder Examination Questionnaire, the Eating Pathology Symptom Inventory, the Frost Multidimensional Perfectionism Scale, the Beck Depression Inventory, and the Penn State Worry Questionnaire at pre-and post-ten weeks of treatment. Using the Thought Inventory, participants collaborated with study therapists to identify ED-related thinking errors and rate the degree of belief in these thoughts on a scale of 0 to 100 %. ResultsCognitions primarily contained self-judgments, food rules, and concern over shape, while catastrophizing/fortune telling, emotional reasoning, and should/must statements were the most common types of thinking errors. Belief in cognitions significantly decreased over treatment and change in thought belief was positively associated with change in ED symptoms. ConclusionThe Thought Inventory shows promise as a personalized measure. Future research should explore whether ED cognitions, assessed in this manner, are a mechanism of change in ED treatment.

Identifying environmental impacts on planktonic algal proliferation and associated risks: a five-year observation study in Danjiangkou Reservoir, China

Understanding the risks of planktonic algal proliferation and its environmental causes is crucial for protecting water quality and controlling ecological risks. Reservoirs, due to the characteristics of slow flow rates and long hydraulic retention times, are more prone to eutrophication and algal proliferation. Chlorophyll-a (Chl-a) serves as an indicator of planktonic algal biomass. Exploring the intricate interactions and driving mechanisms between Chl-a and the water environment, and the potential risks of algal blooms, is crucial for ensuring the ecological safety of reservoirs and the health of water users. This study focused on the Danjiangkou Reservoir (DJKR), the core water source of the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC). The multivariate statistical methods and structural equation modeling were used to explore the relationships between chlorophyll-a (Chl-a) contents and water quality factors and understand the driving mechanisms affecting Chl-a variations. The Copula function and Bayesian theory were combined to analyze the risk of changes in Chl-a concentrations at Taocha (TC) station, which is the core water source intake point of the MRSNWDPC. The results showed that the factors driving planktonic algal proliferation were spatially heterogeneous. The main factors affecting Chl-a concentrations in Dan Reservoir (DR) were water physicochemical factors (water temperature, dissolved oxygen, pH value, and turbidity) with a total contribution rate of 60.18%, whereas those in Han Reservoir (HR) were nutrient factors (total nitrogen, total phosphorus, and ammonia nitrogen) with a total contribution rate of 73.58%. In TC, the main factors were water physicochemical factors (turbidity, pH, and water temperature) and nutrient factors (total phosphorus) with total contribution rates of 39.76% and 45.78%, respectively. When Chl-a concentrations in other areas of the DJKR ranged from the minimum to the uppermost quartile, the probabilities that Chl-a concentrations at the TC station exceeded 3.4 μg/L (the benchmark value of Chl-a for lakes in the central-eastern lake area of China) owing to the influence of these areas were all less than 10%. Thus, the risk of planktonic algal proliferation at the MRSNWDPC intake point is low. This study developed an integrated framework to investigate spatiotemporal changes in algal proliferation and their driving factors in reservoirs, which can be used to support water quality management in mega hydro projects.

Lithium-Ion Battery Health Assessment Method Based on Double Optimization Belief Rule Base with Interpretability

Health assessment is necessary to ensure that lithium-ion batteries operate safely and dependably. Nonetheless, there are the following two common problems with the health assessment models for lithium-ion batteries that are currently in use: inability to comprehend the assessment results and the uncertainty around the chemical reactions occurring inside the battery. A rule-based modeling strategy that can handle ambiguous data in health state evaluation is the belief rule base (BRB). In existing BRB studies, experts often provide parameters such as the initial belief degree, but the parameters may not match the current data. In addition, random global optimization methods may undermine the interpretability of expert knowledge. Therefore, this paper proposes a lithium-ion battery health assessment method based on the double optimization belief rule base with interpretability (DO-BRB-I). First, the belief degree is optimized according to the data distribution. Then, to increase accuracy, belief degrees and other parameters are further optimized using the projection covariance matrix adaptive evolution strategy (P-CMA-ES). At the same time, four interpretability constraint strategies are suggested based on the features of lithium-ion batteries to preserve interpretability throughout the optimization process. Finally, to confirm the efficacy of the suggested approach, a sample of the health status assessment of the B0006 lithium-ion battery is provided.

Supplemental Material for Bridging the Gap Between Subjective Probability and Probability Judgments: The Quantum Sequential Sampler

Supplemental Material for Bridging the Gap Between Subjective Probability and Probability Judgments: The Quantum Sequential Sampler

Analysis of multiple gravitational influences on interregional population migration in South Korea

ABSTRACT Migration is the result of complex interactions between different socio-economic factors in different areas. This study analyses the multiple gravitational influences on interregional migration in South Korea. Spatial econometric models are employed to incorporate spatial interactions between multiple socio-economic gravitational influences into spatial weight matrices. Using Bayesian posterior probabilities to evaluate models incorporating different gravitational influences, this study finds that the primary driver of migration is the difference in human capital conditions between areas. Furthermore, it suggests that the simultaneous consideration of differences in both economic and human capital conditions across areas increases the attractiveness of certain areas for migration.

Physical model and Bayesian theory based hydraulic component fault diagnosis method

Hydraulic components are integral parts of hydraulic systems, and their failures directly impact the normal operation of hydraulic systems. Due to the dynamic variation of working pressure in hydraulic components with load, it is challenging to diagnose faults of hydraulic components under dynamic pressure conditions using existing methods. This paper focuses on the research of internal leakage faults in hydraulic directional control valves and proposes a fault diagnosis method based on physical model and Bayesian theory for hydraulic components. Firstly, this method investigates the working fault mechanism of hydraulic directional control valves and constructs an internal leakage model. Then, correlation analysis is introduced to select fault feature signals, and principal component analysis is adopted to construct internal leakage features. Finally, based on Bayesian model, Markov chain Monte Carlo sampling, and internal leakage features, the parameters of the internal leakage model are estimated to achieve fault diagnosis of internal leakage in hydraulic directional control valves. Experimental results demonstrate that compared to several traditional fault diagnosis methods, the proposed method exhibits higher adaptability and accuracy under dynamic pressure conditions. This method, based on the internal leakage model and Bayesian theory, realizes fault diagnosis of hydraulic components under dynamic pressure conditions, avoiding the shortcomings of deep learning methods that rely on a large amount of fault data to train fault models.

Quantitative risk analysis of road transportation of hazardous materials in coastal areas

Given the complex climate conditions in coastal areas and their role as key transportation hubs for hazardous chemicals, this study proposes a method to quantitatively and comprehensively evaluate transportation risks. Initially, accident data were analyzed to identify risk factors from five aspects: human, vehicle, materials, environment, and management, based on system safety theory. Subsequently, a risk analysis model was developed using Decision-making Trial and Evaluation Laboratory, interpretive structural model theory, and Bayesian theory to quantitatively assess accident risk levels. The model was applied to a case involving a hazardous chemical accident on a cross-sea bridge, where Bayesian backward reasoning was used to analyze the sensitivity and importance of risk factors. This approach facilitated the key risk factors affecting the safety of hazardous chemical transportation systems. Notably, the study incorporated scenarios involving hazardous material transport vehicles crossing sea bridges into the risk assessment framework, offering valuable insights for management authorities. It also considered the impact of strong side winds-a factor often overlooked-in hazardous material transport. The validation process demonstrated that the method accurately quantifies the risk of hazardous chemical transportation and identifies the key factors influencing accident occurrence. The research highlights that strong gusts of wind significantly impact safety, and human factors are crucial in the overall risk system.

Convergence rates of Metropolis–Hastings algorithms

AbstractGiven a target probability density known up to a normalizing constant, the Metropolis–Hastings algorithm simulates realizations from a Markov chain which are eventual realizations from the target probability density. A key element for ensuring a reliable Metropolis–Hastings simulation experiment is understanding how quickly the simulation will generate a representative sample from target density. This corresponds to understanding the convergence properties of the Metropolis–Hastings Markov chain. State‐of‐the‐art methods for convergence analysis of Metropolis–Hastings algorithms are considered and reviewed. Practically important topics are discussed for an interdisciplinary audience. This includes convergence properties in high dimensions, proper tuning, initialization, and limitations of current convergence analyses.This article is categorized under: Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo Statistical and Graphical Methods of Data Analysis > Monte Carlo Methods Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory

Self correction technique for amplitude and phase errors of underwater acoustic arrays based on variable decibel Bayesian inference

Abstract Direction of arrival (DOA) estimation of underwater targets is an important research direction in the field of underwater acoustic array signal processing. Due to factors such as assembly errors and component aging, amplitude and phase errors between each channel of the array are common, resulting in distortion of the array flow pattern matrix. At the same time, the non-uniform distribution of noise intensity at the positions of each array element further increases the difficulty of DOA for underwater targets. Based on the characteristics of sparse spatial distribution of underwater targets, this paper uses the flexibility of hyperparametric modeling to construct an array observation signal graph model. On this basis, the effects of amplitude and phase errors and non-uniform noise on the array model are added, and a comprehensive observation model with clear distribution types of amplitude and phase errors, signals, and noise is established. The variational Bayesian inference theory is used to obtain closed solutions of various variables and parameters, so as to achieve joint estimation of target azimuth and error parameters. Simulation results and sea trial data processing results show that the algorithm can achieve amplitude and phase error self correction while completing DOA estimation in the absence of prior information on the number of sources.

DSD-related attitudes and beliefs held by pediatric endocrinologists and urologists: Insights from the North American disorders/differences of sex development (DSD) clinician survey

Disorders/Differences of Sex Development (DSD) encompass congenital conditions with atypical development of chromosomal, gonadal, or anatomical sex. Due to the rarity and complexity of these conditions, strong evidence for clinical practices is scarce, leading to controversies in management. This study, part of a broader project, examines changes over time in the attitudes and beliefs of DSD healthcare providers, focusing on factors contributing to patients' life satisfaction and the influence of medical specialty, gender, and age. Participants included active members of the Pediatric Endocrine Society (PES) and the Societies for Pediatric Urology (SPU) at three time points: 2003-2004 (T1), 2010-2011 (T2), and 2020 (T3). A survey covering predictors of patient life satisfaction, attitudes and beliefs regarding DSD care and outcomes, and participant characteristics was administered. Data were analyzed using descriptive statistics and Generalized Estimating Equations (GEE). Demographics: Participation rates were 56% (PES) and 64.7% (SPU) at T1, 41.1% (PES) and 52.3% (SPU) at T2, and 25.6% (PES) and 51.2% (SPU) at T3. Most participants were male (T1: 70.6%, T2: 61.7%, T3: 70.6%). Factors Affecting Life Satisfaction: Both endocrinologists and urologists ranked "gender identity consistent with assigned sex" as most important. Over time, the endorsement of some factors, such as performing genital surgery at Centers of Excellence, increased, while others, like the influence of prenatal androgen exposure determining gender identity, varied by specialty and gender. Attitudes and Beliefs: Across 18 statements, responses indicated three clusters with strong agreement, moderate agreement, and strong disagreement. Statements on the importance of family background and avoiding gender discordance were consistently endorsed, while those on delaying hypospadias repair until consent were least endorsed. The study highlights variability in beliefs about DSD management over time, influenced by specialty, gender, and age. Despite consensus on some care principles, discrepancies remain, particularly regarding the impact of prenatal androgens and the timing of surgical interventions. These findings underscore the need for regular interdisciplinary communication to align clinical practices with evidence-based guidelines and address subjective beliefs. The survey illustrates evolving perspectives among DSD healthcare providers, emphasizing the need for continued dialogue and education to bridge gaps between clinical evidence and practice. Collaborative efforts, such as the international I-DSD and the U.S. DSD Translational Research Network, are crucial for advancing patient-centered care in this field.

Enhancing the synergy governance capability of urban communities based on Bayesian network: A case of Jinan city

The rapid pace of urbanization has brought about a plethora of challenges for urban communities, prompting the advocacy of collaborative governance as a means to tackle management issues effectively. However, fostering strong collaboration among multiple governance entities remains a significant hurdle in urban communities governance. This study addresses this challenge by exploring the complex structure of urban communities' collaborative governance systems, with a focus on enhancing collaborative governance capability. Drawing on Bayesian network (BN) theory and utilizing the case of Jinan, this article investigates the intrinsic mechanisms of achieving collaboration in urban communities. This study establishes a Bayesian network model for collaborative governance of urban communities, providing a visual representation of the intricate interactions among multiple governance entities. We employ a data-knowledge-driven approach for BN structure learning. This approach tackles issues such as the lack of cyclic dependency capability and the impact of small datasets on model accuracy. Furthermore, we analyze the collaborative mechanism through Bayesian reasoning. The findings underscore the pivotal entities and five key factors that significantly influence collaborative governance capability. Lastly, policy implications are drawn to enhance the collaborative governance capability of urban communities.

Bayesian credibility model with heavy tail random variables: calibration of the prior and application to natural disasters and cyber insurance

Bayesian credibility model with heavy tail random variables: calibration of the prior and application to natural disasters and cyber insurance

An integrated approach to Bayesian weight regulations and multitasking learning methods for generating emotion-based content in the metaverse

This paper introduces an integrated model designed to analyze topics and sentiments in textual data simultaneously, recognizing their interdependence. By tackling challenges such as data scarcity, missing information, and biased distributions, the proposed model effectively captures the dynamic interactions between topics and sentiments within complex datasets. The method combines the use of Convolutional Neural Networks (CNN) for detecting topic-related patterns with a revised Recurrent Neural Network (RNN) to trace the emotional flow within contexts, leveraging the strengths of sequential data processing. These components are integrated within a Bayesian probability framework, modeling the conditional probabilities of sentences expressing specific sentiments and documents being associated with particular topics. The combined feature and state vectors from the CNN and revised RNN within this Bayesian setup enable precise classification and prediction of topics and sentiments. Furthermore, this paper explores innovative research avenues, including sentiment analysis in 3D virtual reality environments and the development of new algorithms that reflect content creation techniques in the metaverse, offering dynamic system construction. This integrated approach not only enhances data-driven decision-making but also unlocks new possibilities for advanced multi-text analysis.