Probabilistic Model Research Articles

Estimation-of-Distribution Algorithms for Multi-Valued Decision Variables

The majority of research on estimation-of-distribution algorithms (EDAs) concentrates on pseudo-Boolean optimization and permutation problems, leaving the domain of EDAs for problems in which the decision variables can take more than two values, but which are not permutation problems, mostly unexplored. To render this domain more accessible, we propose a natural way to extend the known univariate EDAs to this setting. Different from a naïve reduction to the binary case, our approach avoids additional constraints.Since understanding genetic drift is crucial for an optimal parameter choice, we extend the known quantitative analysis of genetic drift to EDAs for multi-valued, categorical variables. Roughly speaking, when the variables take r different values, the time for genetic drift to become significant is r times shorter than in the binary case. Consequently, the update strength of the probabilistic model has to be chosen r times lower now.To investigate how desired model updates take place in this framework, we undertake a mathematical runtime analysis on the r-valued LeadingOnes problem. We prove that with the right parameters, the multi-valued UMDA solves this problem efficiently in O(rln⁡(r)2n2ln⁡(n)) function evaluations. This bound is nearly tight as our lower bound Ω(rln⁡(r)n2ln⁡(n)) shows.Overall, our work shows that our good understanding of binary EDAs naturally extends to the multi-valued setting, and it gives advice on how to set the main parameters of multi-values EDAs.

Sample-imbalanced wafer map defects classification based on auxiliary classifier denoising diffusion probability model

This paper research on the classification task of wafer map defects under imbalanced sample. To improve the number of imbalanced wafer maps, a denoising diffusion probabilistic model with auxiliary classifier is proposed to generate new wafer maps. The developed model has an U-net structure with multiple inputs and outputs, taking wafer images, noise degree and category features as inputs, and outputs predicted noise and prediction category. When performing data augmentation, the trained model gradually removes prediction noise from the initial random noise and obtains new wafers. Finally, the balanced wafer defect maps are classified using a residual neural network. The proposed auxiliary classifier denoising diffusion probabilistic model and residual neural networks (ACDDPM-ResNet) method has validated on the MIR-WM811K dataset and MixedWM38 dataset, and the defect classification results of the imbalanced wafer map are remarkable improved after data augmentation. In addition, the paper also discusses and analyzes the influence of the maximum noise addition steps and the data augmentation size on the accuracy of wafer classification. It is further validated that the proposed wafer map classification method based on data augmentation can solve the classification problem caused by sample imbalance.

Probabilistic modeling of renewable energy sources in smart grids: A stochastic optimization perspective

Probabilistic modeling of renewable energy sources in smart grids: A stochastic optimization perspective

A Spatiotemporal Probabilistic Graphical Model Based on Adaptive Expectation-Maximization Attention for Individual Trajectory Reconstruction Considering Incomplete Observations

Spatiotemporal information on individual trajectories in urban rail transit is important for operational strategy adjustment, personalized recommendation, and emergency command decision-making. However, due to the lack of journey observations, it is difficult to accurately infer unknown information from trajectories based only on AFC and AVL data. To address the problem, this paper proposes a spatiotemporal probabilistic graphical model based on adaptive expectation maximization attention (STPGM-AEMA) to achieve the reconstruction of individual trajectories. The approach consists of three steps: first, the potential train alternative set and the egress time alternative set of individuals are obtained through data mining and combinatorial enumeration. Then, global and local potential variables are introduced to construct a spatiotemporal probabilistic graphical model, provide the inference process for unknown events, and state information about individual trajectories. Further, considering the effect of missing data, an attention mechanism-enhanced expectation-maximization algorithm is proposed to achieve maximum likelihood estimation of individual trajectories. Finally, typical datasets of origin-destination pairs and actual individual trajectory tracking data are used to validate the effectiveness of the proposed method. The results show that the STPGM-AEMA method is more than 95% accurate in recovering missing information in the observed data, which is at least 15% more accurate than the traditional methods (i.e., PTAM-MLE and MPTAM-EM).

N-Gram based Assamese Question Pattern Extraction and Probabilistic Modelling

N-gram probabilities provide valuable information in understanding, processing, and modelling various natural language processing tasks. They assign probabilities to the sequences of words and subsequently to the whole sentence. Such information is very essential to make more accurate predictions in machine learning based systems. Here in this paper we worked on finding Parts-of-Speech (PoS) sequence based Assamese question patterns. We derived the unique bi-grams and tri-grams of PoSs occurring in the patterns and also extracted the probabilities of them. We then tried to find the unique PoS patterns of Assamese questions. We also have tried to incorporate the probabilities of unique bi-grams and tri-grams and the combined bi-grams and tri-grams probabilities of all patterns. Our work is a novel approach of finding the probabilities of bi-grams and tri-grams of the patterns occurring in Assamese questions.

An appraisal of statistical and probabilistic models in highway pavements

Accurate performance prediction is crucial for safe and efficient travel on highway pavements. Within pavement engineering, statistical models play a pivotal role in understanding pavement behavior and durability. This comprehensive study critically evaluates a spectrum of statistical models utilized in pavement engineering, encompassing mechanistic-empirical, Weibull distribution, Markov chain, regression, Bayesian networks, Monte Carlo simulation, artificial neural networks, support vector machines, random forest, decision tree, fuzzy logic, time series analysis, stochastic differential equations, copula, hidden semi-Markov, generalized linear, survival analysis, response surface methodology and extreme value theory models. The assessment meticulously examines equations, parameters, data prerequisites, advantages, limitations, and applicability of each model. Detailed discussions delve into the significance of equations and parameters, evaluating model performance in predicting pavement distress, performance assessment, design optimization, and life-cycle cost analysis. Key findings emphasize the critical aspects of accurate input parameters, calibration, validation, data availability, and model complexity. Strengths, limitations, and applicability across various pavement types, materials, and climate conditions are meticulously highlighted for each model. Recommendations are outlined to enhance the effectiveness of statistical models in pavement engineering. These suggestions encompass further research and development, standardized data collection, calibration and validation protocols, model integration, decision-making frameworks, collaborative efforts, and ongoing model evaluation. Implementing these recommendations is anticipated to enhance prediction accuracy and enable informed decision-making throughout highway pavement design, construction, maintenance, and management. This study is anticipated to serve as a valuable resource, providing guidance and insights for researchers, practitioners, and stakeholders engaged in asphalt engineering, facilitating the effective utilization of statistical models in real-world pavement projects.

Digitalization of corporate finance and firm performance: global evidence and analysis

Purpose As of now, the digitization of corporate finance presents a paradigm shift in business strategy, innovation, financing and managerial capability around the globe. However, the prevailing finance scholarly works hardly document the impact of the digitalization of corporate finance on firm performance with global evidence and analysis. Hence, the contemporary debate on whether firm performance is genuinely stimulated because of the digitalization of corporate finance or not has been a pressing issue in the relevant literature. Therefore, the purpose of this study is to identify a data-driven, concise response to an unaddressed finance issue if the performance of high-digitalized firms (HDFs) outperforms that of their counterpart peers for wealth maximization. Design/methodology/approach The first stage test models examine the firm performance of relatively high-digitalized firms as opposed to low-digitalized firms based on the system GMM. The second stage test of the probabilistic (logit) model infers that the probability of being HDFs explores because of better performance. Then, the authors execute robust checks based on the different quantile regressions and Z-score-based system GMM. In addition, the authors recheck and present the test results of the fixed effect and random effect to capture time-invariant individual heterogeneity. Finally, the supplementary test findings of firms’ credit strength by using Altman five- and four-factor Z-score models are presented. Findings By using cross-country panel analysis as 15 years’ test bed for HDFs and low digitalized firms (LDFs), the test results indicate that the overall firm performance of a digitalized firm is significantly better than that of a non-digitalized firm. The global evidence documents that HDFs are exposed to higher values and are financially more persistent as compared to their counterparts. The finding is remarkably concomitant across several possible subsample analysis, such as country–industry–size–period analysis. Practical implications This study can be remarkably effective in encouraging managers, policymakers and investors to acknowledge the need for adopting the required digitalization. Overall, this original study addresses a core research gap in the corporate finance literature and remarkably provides further direction to rethink the assumptions of firm digitalization on additive value and thereby identify optimal decisions for wealth maximization. The findings also imply that investors require an additional risk premium if they invest in relatively LDFs, which have relatively lower market value and weaker firm performance. Originality/value From an investors point of view, the academic novelty contributes to an innovative and unsettled issue on the impact of digitization of corporate finance on firm performance because there is a new question of high or low digitization of corporate finance in the global market. Hence, this academic novelty contributes to sharing global evidence of the digitalization of corporate finance and its effect on firm performances. In addition, an intensive critical review analysis is conducted based on the most recent and relevant scholarly works published in the top-tier journals of finance and business stream to fix the hypothesis. Overall, this study addresses a core research gap in the corporate finance literature; notably provides further direction to rethink firm digitalization; and thereby identifies optimal decisions for shareholders’ wealth maximization.

Decoding the Atmosphere: Optimising Probabilistic Forecasts with Information Gain

Probabilistic prediction models exist to reduce surprise about future events. This paper explores the evaluation of such forecasts when the event of interest is rare. We review how the family of Brier-type scores may be ill-suited to evaluate predictions of rare events, and we offer an alternative to information-theoretical scores such as Ignorance. The reduction in surprise provided by a set of forecasts is represented as information gain, a frequent loss function in machine learning training, meaning the reduction in ignorance over a baseline having received a new forecast. We evaluate predictions of a synthetic dataset of rare events and demonstrate the differences in interpretation of the same datasets depending on whether the Brier or Ignorance score is used. While the two types of scores are broadly similar, there are substantial differences in interpretation at extreme probabilities. Information gain is measured in units of bits, an irreducible unit of information, that allows forecasts of different variables to be comparatively evaluated fairly. Further insight from information-based scores is gained via a similar reliability–discrimination decomposition as found in Brier-type scores. We conclude by crystallising multiple concepts to better equip forecast-system developers and decision-makers with tools to navigate complex trade-offs and uncertainties that characterise meteorological forecasting. To this end, we also provide computer code to reproduce data and figures herein.

A study of blood glucose determination by means of an interlaboratory test

Through inter-laboratory comparison, based on the measurement process and measuring equipment conducted according to predetermined criteria, it is possible to evaluate the performance of the participants, which is known as qualification testing and is regulated in accordance with ISO 17043:2017/2018.In the medical field, the term “external quality assessment” is used for qualification testing schemes or wider programs where samples (standard, certified, attested), products, artefacts, pieces of equipment, measurement standards, data sets or other information are used for qualification testing. The processing of measurement data, which in many cases includes a random error detector along with a systematic error detector, is carried out using mathematical statistics methods, including statistical methods that are less sensitive to small deviations from the basic assumptions surrounding the probabilistic model, which are known as robust methods. Robust methods are a powerful tool for demonstrating competence in performing tests and measurements related to qualification testing schemes.

VQ-CAD: Computer-Aided Design model generation with vector quantized diffusion

Computer-Aided Design (CAD) software remains a pivotal tool in modern engineering and manufacturing, driving the design of a diverse range of products. In this work, we introduce VQ-CAD, the first CAD generation model based on Denoising Diffusion Probabilistic Models. This model utilizes a vector quantized diffusion model, employing multiple hierarchical codebooks generated through VQ-VAE. This integration not only offers a novel perspective on CAD model generation but also achieves state-of-the-art performance in 3D CAD model creation in a fully automatic fashion. Our model is able to recognize and incorporate implicit design constraints by simply forgoing traditional data augmentation. Furthermore, by melding our approach with CLIP, we significantly simplify the existing design process, directly generate CAD command sequences from initial design concepts represented by text or sketches, capture design intentions, and ensure designs adhere to implicit constraints.

Reliability-Based Design Optimization for a Vertical-Type Breakwater with an Emphasis on Sliding, Overturn, and Collapse Failure

To promote the application of reliability-based design within the Korean coastal engineering community, the author conducted reliability analyses and optimized the design of a vertical-type breakwater, considering multiple limit states in the seas off of Pusan and Gunsan – two representative ports in Korea. In this process, rather than relying on design waves of a specific return period, the author intentionally avoided such constraints. Instead, the author characterized the uncertainties associated with wave force, lift force, and overturning moment – key factors significantly influencing the integrity of a vertical-type breakwater. This characterization was achieved by employing a probabilistic model derived from the frequency analysis results of long-term in-situ wave data. The limit state of the vertical-type breakwater encompassed sliding, overturning, and collapse failure, with the close interrelation between wave force, lift force, and moment described using the Nataf joint probability distribution. Simulation results indicate, as expected, that considering only sliding failure underestimates the failure probability. Furthermore, it was shown that the failure probability of vertical-type breakwaters cannot be consistently secured using design waves with a specific return period. In contrast, breakwaters optimally designed to meet the reliability index requirement of <i>β</i>-3.5 to 4 consistently achieve a consistent failure probability across all sea areas.

Probabilistic models for studying the potential spatial distribution of three intertidal decapods in the north of Chile (Taltal, 25°S, Antofagasta region)

Probabilistic models for studying the potential spatial distribution of three intertidal decapods in the north of Chile (Taltal, 25°S, Antofagasta region)

Comparison of probabilistic cruising-for-parking time estimation models

Models of parking search time predominantly rely on the binomial approximation, the calculation of which is dependent solely on the occupancy of the parking network. This paper sheds light on the importance of accounting for competition between parking searchers and the effects of dynamically changing occupancy. To this end, we investigate the accuracy of two probabilistic models, namely the binomial approximation and a model proposed by Cao and Menéndez (CaM). We utilize an agent-based microscopic parking simulation model as a benchmark for comparison purposes. Our findings highlight the significance of considering the competitive dynamics among drivers in the quest for parking spaces and the fluctuations in parking occupancy rates over short time intervals. We highlight the ranges of occupancies for which the binomial approximation and the CaM model provide more reliable estimations of parking search times. The CaM model provides, in general, more accurate estimations of search times since it accounts for various parking network characteristics such as competition and varying occupancy. The binomial approximation underestimates parking search times more substantially at high occupancies with a large number of searching vehicles, whereas the CaM model performs worse for lower spacing between parking spots and lower parking occupancies. Insights from this research are then used to discuss policy implications of accounting for competition and short-term occupancy variations when designing either parking models or parking-related control strategies.

Exploring the Relationship Between Key Factors and Victory and Defeat in Volleyball Matches using Bayesian Network Modeling

Identifying key factors contributing to victory and defeat in volleyball matches using Bayesian network modeling involves probabilistic graphical modeling to analyze the interrelationships among various factors and their influence on match outcomes. By integrating data on player performance, team strategies, match conditions, and opponent characteristics, Bayesian networks can uncover complex dependencies and predict the likelihood of winning or losing based on different scenarios. The research conducted on exploring the relationship between key factors and victory and defeat in volleyball matches using Bayesian network modeling. Through the utilization of Bayesian network modeling, this study aims to elucidate the intricate interplay between different variables and their impact on match outcomes in the context of volleyball. The research employs a Hidden Markov Process Bayesian Network (HMPBN) to model the temporal dependencies and uncertainties inherent in volleyball matches. The dataset comprises a comprehensive collection of volleyball match data, encompassing various observable variables such as points scored, successful serves, defensive plays, and more. The simulation setup involves training the HMPBN model on a dataset consisting of 1000 instances and evaluating its performance on a separate test dataset of 500 instances. The model parameters, including the transition matrix, emission matrix, and initial state distribution, are determined through rigorous statistical estimation techniques. The results of the simulation experiments provide valuable insights into the probabilities associated with different actions and their influence on match outcomes. The study reveals that successful serves have a high probability of contributing to match victories, with an average success rate of 80% in the dataset. Conversely, defensive plays exhibit a lower success rate, with an average probability of success of 60%. The Hidden Markov Process Bayesian Network (HMPBN) achieves an overall classification accuracy of 85% on the test dataset, demonstrating its effectiveness in predicting match outcomes.

Evaluation of Teaching Effectiveness Based on Bayesian Network Algorithm in Teaching and Learning Process in Higher Education Institutions

The Bayesian network algorithm plays a pivotal role in enhancing the teaching and learning process in higher education institutions by facilitating personalized and adaptive learning experiences. This algorithm leverages probabilistic graphical models to represent relationships among various educational variables, such as student performance, learning resources, and teaching methods. Additionally, Bayesian networks aid instructors in optimizing course design and instructional strategies by identifying areas for improvement and evaluating the effectiveness of different teaching approaches. This paper explores the application of Hidden Markov Chain Bayesian Networks (HMCBN) in analyzing teaching effectiveness within higher education institutions. Teaching effectiveness is a multifaceted concept influenced by various factors, including teaching strategies, student characteristics, and learning outcomes. Traditional statistical methods often struggle to capture the dynamic and interdependent nature of these factors. Through the construction of HMCBN models, this study investigates the probabilistic relationships between teaching strategies, student characteristics, learning outcomes, and teaching effectiveness. For teaching strategies, the probabilities [0.3, 0.5, 0.2] indicate the likelihood of each strategy being employed, suggesting that Strategy 2 is the most frequently utilized, followed by Strategy 1 and Strategy 3. Similarly, the probabilities [0.4, 0.3, 0.3] for student characteristics suggest a relatively balanced distribution among the three characteristic categories. In terms of learning outcomes, the conditional probabilities reflect the influence of both teaching strategies and student characteristics on the outcomes achieved. For each combination of teaching strategy and student characteristic, the probabilities [0.1, 0.3, 0.6], [0.4, 0.5, 0.1], [0.3, 0.4, 0.3], [0.2, 0.5, 0.3], [0.3, 0.4, 0.3], [0.4, 0.3, 0.3], [0.1, 0.7, 0.2], [0.4, 0.5, 0.1], and [0.5, 0.4, 0.1] represent the probabilities of achieving different learning outcomes given specific combinations of teaching strategies and student characteristics.

A Blended Teaching Model of College English Based on Deep Learning

Blending traditional face-to-face instruction with online learning components, this model aims to enhance student engagement and improve learning outcomes. This paper introduces a novel blended teaching model for college English courses, integrating Conditional Random Field (CRF) techniques with deep learning frameworks. Blended learning, combining traditional face-to-face instruction with online learning components, has gained traction in higher education for its potential to enhance student engagement and learning outcomes. CRF blended teaching integrates the principles of conditional random fields, a powerful probabilistic graphical model, with traditional teaching methodologies to enhance learning outcomes. Unlike conventional teaching approaches that often rely on linear or static instructional methods, CRF blended teaching embraces a more dynamic and adaptive framework. By leveraging CRF, which models the dependencies between different instructional variables, educators can design personalized learning experiences tailored to individual student needs and learning styles. This approach enables the seamless integration of diverse instructional modalities, including face-to-face instruction, online resources, and interactive learning activities. The proposed model CRF algorithms to effectively capture the sequential dependencies inherent in language learning tasks, such as sentence parsing and part-of-speech tagging. By integrating deep learning architectures, including recurrent neural networks (RNNs) and transformers, model with large-scale linguistic corpora to improve accuracy and generalization. The model achieves a 15% increase in students' English proficiency scores and a 25% improvement in comprehension compared to traditional teaching methods, underscoring its effectiveness in enhancing learning outcomes.

Spatial spread of COVID-19 during the early pandemic phase in Italy

Quantifying the potential spatial spread of an infectious pathogen is key to defining effective containment and control strategies. The aim of this study is to estimate the risk of SARS-CoV-2 transmission at different distances in Italy before the first regional lockdown was imposed, identifying important sources of national spreading. To do this, we leverage on a probabilistic model applied to daily symptomatic cases retrospectively ascertained in each Italian municipality with symptom onset between January 28 and March 7, 2020. Results are validated using a multi-patch dynamic transmission model reproducing the spatiotemporal distribution of identified cases. Our results show that the contribution of short-distance (≤10km)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\le 10 km)$$\\end{document} transmission increased from less than 40% in the last week of January to more than 80% in the first week of March 2020. On March 7, 2020, that is the day before the first regional lockdown was imposed, more than 200 local transmission foci were contributing to the spread of SARS-CoV-2 in Italy. At the time, isolation measures imposed only on municipalities with at least ten ascertained cases would have left uncontrolled more than 75% of spillover transmission from the already affected municipalities. In early March, national-wide restrictions were required to curb short-distance transmission of SARS-CoV-2 in Italy.

Space-time heterogeneity of measles, mumps, and rubella vaccination indicators in children from BrazilHeterogeneidad espaciotemporal de los indicadores de inmunización con la vacuna triple viral en la población infantil de Brasil

To evaluate immunization coverage and dropout rates of the measles, mumps, and rubella vaccine across Brazilian regions. This ecological study employing a space-time approach used data from the National Immunization Program (PNI) and the Live Births Information System (SINASC). We estimated the annual variation (2014 to 2021) in immunization coverage and dropout rate of the measles, mumps, and rubella vaccine in children aged 12 and 15 months in the 5 570 Brazilian municipalities. Statistical analysis was conducted for the municipalities contained in each Brazilian region using the space-time scan technique, considering the Poisson probabilistic model and the hypothesis that municipalities with lower immunization coverage or high dropout rates would form space-time clusters. From 2014 to 2021, 38.3% and 12.9% of municipalities achieved a coverage ≥ 95.0% in the first and second doses respectively; 53.6% of municipalities had a high dropout rate (20.0% to 49.9%) and 37.2% had a very high dropout rate. The Northeast region had primary clusters for low coverage of the first (2018 to 2021) and second vaccine doses (2020 to 2021) with higher relative risks (RR) compared to other primary clusters. The RR for high dropout rate was elevated in all Brazilian regions (1.57 to 26.23). In some Brazilian regions, the dropout rate has been high since 2014, signaling a risk of measles resurgence. In turn, space-time analysis indicated lower vaccination coverage in 2020, suggesting the influence of the COVID-19 pandemic.

Image-driven machine learning for automatic characterization of grain size and distribution in smart vanadium dioxide thin films

This study focuses on characterizing grain size and distribution in vanadium dioxide (VO2) thin films using Atomic Force Microscope (AFM) images. Traditional segmentation methods face challenges in accurately detecting grains, and this paper addresses these limitations by employing machine learning-based classification approaches, defining three classes: grains, joints or dividing lines, and the background. Various classification methods, including supervised, unsupervised, and thresholding-based approaches, were investigated, demonstrating the superiority of machine learning-based methods over traditional segmentation techniques. Deep Learning (DL) models, such as Convolutional Neural Networks (CNN), Deep Belief Networks (DBN), and Deep Boltzmann Machines (DBM), achieved the best results in accuracy and precision. The DBN algorithm, in particular, outperformed other methods with a 93.66% accuracy in grain detection. This could be attributed to its probabilistic modeling and effective handling of challenging pixels, making it a powerful tool for accurate grain size and distribution analysis.

Selection of optimal intensity measures for seismic performance evaluation of underground utility tunnel and internal pipeline system

Seismic intensity measures (IMs), which are closely related to both earthquake hazards and structural responses, play a critical role in the performance-based earthquake engineering (PBEE) framework. This paper aims to investigate and define the most representative IMs for probabilistic seismic demand model (PSDM) for the longitudinal utility tunnel and internal pipeline system. Similar to the requirements by the Chinese Code for seismic design of urban rail transit structures, the utility tunnel in this study is assumed being buried in four typical engineering site classes for the analyses. Soil-tunnel-pipeline interactions are simulated using a double-beam system resting on a nonlinear foundation. Soil-tunnel interaction, tunnel-pipeline interaction and joint connections are modeled as nonlinear springs with different mechanical properties. A series of 17 pairs of outcrop ground-motion records and 27 commonly used IMs are selected in the analyses. Engineering demand parameter (EDP) is defined based on the maximum joint opening and the maximum strain of the utility tunnel and internal pipeline, respectively. The selected IMs for predicting the seismic responses of tunnel-pipeline systems are tested using different criteria, i.e., correction, efficiency, practicality, and proficiency. The final decision-making procedure employs a fuzzy multi-criteria decision method. The numerical results indicate that: (1) The velocity-related IMs are the most representative IMs based on the four criteria for structures buried in four sites of different conditions; (2) The optimal seismic IMs vary with different criteria, site classes and structure types. According to the fuzzy multi-criteria decision approach, optimal IMs for the tunnel-pipeline system are PGV, PGV, EPV and HI for site classes I – IV, respectively. The proposed optimal IMs can be utilized to construct seismic fragility curves of utility tunnel, internal pipeline and tunnel-pipeline systems in four different site classes.