The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering最新文献

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Nested Sampling for Detection and Localization of Sound Sources Using a Spherical Microphone Array 利用球形麦克风阵列进行嵌套采样以检测和定位声源
Ning Xiang, Tomislav Jasa
{"title":"Nested Sampling for Detection and Localization of Sound Sources Using a Spherical Microphone Array","authors":"Ning Xiang, Tomislav Jasa","doi":"10.3390/psf2023009026","DOIUrl":"https://doi.org/10.3390/psf2023009026","url":null,"abstract":"","PeriodicalId":506244,"journal":{"name":"The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering","volume":"16 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141119219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Manifold-Based Geometric Exploration of Optimization Solutions 基于范式的优化解决方案几何探索
Guillaume Lebonvallet, Faicel Hnaien, Hichem Snoussi
{"title":"Manifold-Based Geometric Exploration of Optimization Solutions","authors":"Guillaume Lebonvallet, Faicel Hnaien, Hichem Snoussi","doi":"10.3390/psf2023009025","DOIUrl":"https://doi.org/10.3390/psf2023009025","url":null,"abstract":"","PeriodicalId":506244,"journal":{"name":"The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering","volume":"29 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140967629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Analysis of Ecological Networks: Linear Inverse Modeling and Information Theory Tools 生态网络分析:线性逆建模和信息论工具
Valérie Girardin, Théo Grente, Nathalie Niquil, P. Regnault
{"title":"Analysis of Ecological Networks: Linear Inverse Modeling and Information Theory Tools","authors":"Valérie Girardin, Théo Grente, Nathalie Niquil, P. Regnault","doi":"10.3390/psf2023009024","DOIUrl":"https://doi.org/10.3390/psf2023009024","url":null,"abstract":": In marine ecology, the most studied interactions are trophic and are in networks called food webs. Trophic modeling is mainly based on weighted networks, where each weighted edge corresponds to a flow of organic matter between two trophic compartments, containing individuals of similar feeding behaviors and metabolisms and with the same predators. To take into account the unknown flow values within food webs, a class of methods called Linear Inverse Modeling was developed. The total linear constraints, equations and inequations defines a multidimensional convex-bounded polyhedron, called a polytope, within which lie all realistic solutions to the problem. To describe this polytope, a possible method is to calculate a representative sample of solutions by using the Monte Carlo Markov Chain approach. In order to extract a unique solution from the simulated sample, several goal (cost) functions—also called Ecological Network Analysis indices—have been introduced in the literature as criteria of fitness to the ecosystems. These tools are all related to information theory. Here we introduce new functions that potentially provide a better fit of the estimated model to the ecosystem.","PeriodicalId":506244,"journal":{"name":"The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering","volume":"45 32","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140448898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Preconditioned Monte Carlo for Gradient-Free Bayesian Inference in the Physical Sciences 物理科学中无梯度贝叶斯推理的预条件蒙特卡罗
M. Karamanis, U. Seljak
{"title":"Preconditioned Monte Carlo for Gradient-Free Bayesian Inference in the Physical Sciences","authors":"M. Karamanis, U. Seljak","doi":"10.3390/psf2023009023","DOIUrl":"https://doi.org/10.3390/psf2023009023","url":null,"abstract":"","PeriodicalId":506244,"journal":{"name":"The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering","volume":"5 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139444284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Nested Sampling—The Idea 嵌套取样--理念
John Skilling
{"title":"Nested Sampling—The Idea","authors":"John Skilling","doi":"10.3390/psf2023009022","DOIUrl":"https://doi.org/10.3390/psf2023009022","url":null,"abstract":"","PeriodicalId":506244,"journal":{"name":"The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering","volume":"34 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139446566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Knowledge-Based Image Analysis: Bayesian Evidences Enable the Comparison of Different Image Segmentation Pipelines 基于知识的图像分析:贝叶斯证据有助于比较不同的图像分割管道
M. L. Moskopp, Andreas Deussen, Peter Dieterich
{"title":"Knowledge-Based Image Analysis: Bayesian Evidences Enable the Comparison of Different Image Segmentation Pipelines","authors":"M. L. Moskopp, Andreas Deussen, Peter Dieterich","doi":"10.3390/psf2023009020","DOIUrl":"https://doi.org/10.3390/psf2023009020","url":null,"abstract":"","PeriodicalId":506244,"journal":{"name":"The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering","volume":"26 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139386315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Inferring Evidence from Nested Sampling Data via Information Field Theory 通过信息场理论从嵌套抽样数据中推断证据
Margret Westerkamp, Jakob Roth, Philipp Frank, W. Handley, T. Enßlin
{"title":"Inferring Evidence from Nested Sampling Data via Information Field Theory","authors":"Margret Westerkamp, Jakob Roth, Philipp Frank, W. Handley, T. Enßlin","doi":"10.3390/psf2023009019","DOIUrl":"https://doi.org/10.3390/psf2023009019","url":null,"abstract":"Nested sampling provides an estimate of the evidence of a Bayesian inference problem via probing the likelihood as a function of the enclosed prior volume. However, the lack of precise values of the enclosed prior mass of the samples introduces probing noise, which can hamper high-accuracy determinations of the evidence values as estimated from the likelihood-prior-volume function. We introduce an approach based on information field theory, a framework for non-parametric function reconstruction from data, that infers the likelihood-prior-volume function by exploiting its smoothness and thereby aims to improve the evidence calculation. Our method provides posterior samples of the likelihood-prior-volume function that translate into a quantification of the remaining sampling noise for the evidence estimate, or for any other quantity derived from the likelihood-prior-volume function.","PeriodicalId":506244,"journal":{"name":"The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering","volume":"50 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139181782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Quantification of Endothelial Cell Migration Dynamics Using Bayesian Data Analysis 利用贝叶斯数据分析量化内皮细胞迁移动力学
A. Hohlstamm, Andreas Deussen, Stephan Speier, Peter Dieterich
{"title":"Quantification of Endothelial Cell Migration Dynamics Using Bayesian Data Analysis","authors":"A. Hohlstamm, Andreas Deussen, Stephan Speier, Peter Dieterich","doi":"10.3390/psf2023009011","DOIUrl":"https://doi.org/10.3390/psf2023009011","url":null,"abstract":"","PeriodicalId":506244,"journal":{"name":"The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139208925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Variational Bayesian Approximation (VBA) with Exponential Families and Covariance Estimation 指数族和协方差估计的变异贝叶斯逼近法 (VBA)
Seyedeh Azadeh Fallah Mortezanejad, A. Mohammad-Djafari
{"title":"Variational Bayesian Approximation (VBA) with Exponential Families and Covariance Estimation","authors":"Seyedeh Azadeh Fallah Mortezanejad, A. Mohammad-Djafari","doi":"10.3390/psf2023009012","DOIUrl":"https://doi.org/10.3390/psf2023009012","url":null,"abstract":"","PeriodicalId":506244,"journal":{"name":"The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering","volume":"118 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139199947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Bayesian Data Analysis Method for an Experiment to Measure the Gravitational Acceleration of Antihydrogen 测量反氢原子引力加速度实验的贝叶斯数据分析方法
D. Hodgkinson, J. Fajans, J. Wurtele
{"title":"A Bayesian Data Analysis Method for an Experiment to Measure the Gravitational Acceleration of Antihydrogen","authors":"D. Hodgkinson, J. Fajans, J. Wurtele","doi":"10.3390/psf2023009009","DOIUrl":"https://doi.org/10.3390/psf2023009009","url":null,"abstract":"","PeriodicalId":506244,"journal":{"name":"The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139223089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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