Results 51 to 60 of about 539,449 (277)
Bayesian model selection and isocurvature perturbations [PDF]
, 2005 Present cosmological data are well explained assuming purely adiabatic perturbations, but an admixture of isocurvature perturbations is also permitted. We use a Bayesian framework to compare the performance of cosmological models including isocurvature ...
Beltrán, María +4 more
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Predictive Bayesian Model Selection [PDF]
American Journal of Mathematical and Management Sciences, 2011 SYNOPTIC ABSTRACTWe investigate the problem of evaluating the goodness of the predictive distributions of Bayesian models. Recently, deviance information criteria (DIC) has been extensively employed in various study areas to evaluate the Bayesian models, thanks to its simplicity of calculation from the posterior simulation outputs. Unfortunately, it is
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Bayesian Computation and Model Selection Without Likelihoods [PDF]
Genetics, 2010 AbstractUntil recently, the use of Bayesian inference was limited to a few cases because for many realistic probability models the likelihood function cannot be calculated analytically. The situation changed with the advent of likelihood-free inference algorithms, often subsumed under the term approximate Bayesian computation (ABC).
Leuenberger, Christoph, Wegmann, Daniel
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Geosciences, 2019 Generalized linear models are routinely used in many environment statistics problems such as earthquake magnitudes prediction. Hu et al. proposed Pareto regression with spatial random effects for earthquake magnitudes.
Hou-Cheng Yang, Guanyu Hu, Ming-Hui Chen
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BMC Medical Research Methodology, 2010 Background Automatic variable selection methods are usually discouraged in medical research although we believe they might be valuable for studies where subject matter knowledge is limited.
Steineck Gunnar +3 more
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Bayesian inference with information content model check for Langevin equations [PDF]
, 2017 The Bayesian data analysis framework has been proven to be a systematic and effective method of parameter inference and model selection for stochastic processes.
Krog, Jens, Lomholt, Michael A.
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Bayesian Variable Selection for Latent Class Models [PDF]
Biometrics, 2010 In this article we develop a latent class model with class probabilities that depend on subject-specific covariates. One of our major goals is to identify important predictors of latent classes. We consider methodology that allows estimation of latent classes while allowing for variable selection uncertainty.
Ghosh, Joyee +2 more
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FEBS Open Bio, EarlyView.Comparative analysis of chloroplast genomes from 14 genera of Thymelaeaceae revealed variation in gene content, ranging from 128 to 142 genes, primarily influenced by IR expansion/contraction events and pseudogenization of ndhF, ndhI, and ndhG. Two large inversions were detected within the large single‐copy region, including a synapomorphic inversion ...
Abdullah +8 more
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Channel estimation using variational Bayesian learning for multi‐user mmWave MIMO systems
IET Communications, 2021 This paper presents a novel variational Bayesian learning‐based channel estimation scheme for hybrid pre‐coding‐employed wideband multiuser millimetre wave multiple‐input multiple‐output communication systems.
Bo Xiao +4 more
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Bayesian Model Selection Based on Proper Scoring Rules
, 2015 Bayesian model selection with improper priors is not well-defined because of the dependence of the marginal likelihood on the arbitrary scaling constants of the within-model prior densities.
Dawid, A. Philip, Musio, Monica
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