Results 21 to 30 of about 15,712,923 (297)
Bayesian parameter inference and model selection by population annealing in systems biology. [PDF]
PLoS ONE, 2014 Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection.
Yohei Murakami
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Water Resources Research, 2023 Bayesian model selection (BMS) and Bayesian model justifiability analysis (BMJ) provide a statistically rigorous framework for comparing competing models through the use of Bayesian model evidence (BME).
Maria Fernanda Morales Oreamuno +2 more
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Robust and parallel Bayesian model selection [PDF]
Computational Statistics & Data Analysis, 2018 Effective and accurate model selection is an important problem in modern data analysis. One of the major challenges is the computational burden required to handle large data sets that cannot be stored or processed on one machine. Another challenge one may encounter is the presence of outliers and contaminations that damage the inference quality.
Michael Minyi Zhang +2 more
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Bayesian Reordering Model with Feature Selection [PDF]
Proceedings of the Ninth Workshop on Statistical Machine Translation, 2014 In phrase-based statistical machine translation systems, variation in grammatical structures between source and target languages can cause large movements of phrases. Modeling such movements is crucial in achieving translations of long sentences that appear natural in the target language.
Alrajeh, Abdullah, Niranjan, Mahesan
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Data-driven prediction and origin identification of epidemics in population networks [PDF]
Royal Society Open Science, 2021 Effective intervention strategies for epidemics rely on the identification of their origin and on the robustness of the predictions made by network disease models. We introduce a Bayesian uncertainty quantification framework to infer model parameters for
Karen Larson +7 more
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Bayesian model averaging: improved variable selection for matched case-control studies
Epidemiology, Biostatistics and Public Health, 2019 Background: The problem of variable selection for risk factor modeling is an ongoing challenge in statistical practice. Classical methods that select one subset of exploratory risk factors dominate the medical research field.
Yi Mu +2 more
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Bayesian model selection for group studies [PDF]
NeuroImage, 2009 Bayesian model selection (BMS) is a powerful method for determining the most likely among a set of competing hypotheses about the mechanisms that generated observed data. BMS has recently found widespread application in neuroimaging, particularly in the context of dynamic causal modelling (DCM).
Klaas Enno Stephan +4 more
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BICOSS: Bayesian iterative conditional stochastic search for GWAS
BMC Bioinformatics, 2022 Background Single marker analysis (SMA) with linear mixed models for genome wide association studies has uncovered the contribution of genetic variants to many observed phenotypes. However, SMA has weak false discovery control.
Jacob Williams +2 more
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Bayesian Model Selection of Unified Neutron Star EOSs in Multi-messenger Era
Yuanzineng kexue jishu, 2023 The equation of state (EoS) of neutron star matter plays a key role in both the structure and evolution of a neutron star. However, as lattice QCD faces significant difficulties in simulating dense matter, only effective models can be relied on to unveil
RUI Xingyu;MIAO Zhiqiang;XIA Chengjun
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BMC Evolutionary Biology, 2010 Background Explicit evolutionary models are required in maximum-likelihood and Bayesian inference, the two methods that are overwhelmingly used in phylogenetic studies of DNA sequence data.
Luo Arong +7 more
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