Results 21 to 30 of about 539,449 (277)
Bayesian Model Selection using Test Statistics [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2008 SummaryExisting Bayesian model selection procedures require the specification of prior distributions on the parameters appearing in every model in the selection set. In practice, this requirement limits the application of Bayesian model selection methodology.
Hu, Jianhua, Johnson, Valen E.
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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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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).
Stephan, Klaas Enno +4 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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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 for Beta Autoregressive Processes [PDF]
, 2010 We deal with Bayesian inference for Beta autoregressive processes. We restrict our attention to the class of conditionally linear processes. These processes are particularly suitable for forecasting purposes, but are difficult to estimate due to the ...
Casarin, R., Leisen, F., Valle, L. Dalla
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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.
Zhang, Michael Minyi +2 more
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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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Bayesian Geoadditive Sample Selection Models
Journal of the Royal Statistical Society Series C: Applied Statistics, 2010 SummarySample selection models attempt to correct for non-randomly selected data in a two-model hierarchy where, on the first level, a binary selection equation determines whether a particular observation will be available for the second level, i.e. in the outcome equation.
Wiesenfarth, Manuel, Kneib, Thomas
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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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