Bayesian Exponential Random Graph Models with Nodal Random Effects [PDF]
, 2015 We extend the well-known and widely used Exponential Random Graph Model (ERGM) by including nodal random effects to compensate for heterogeneity in the nodes of a network.
A. Caimo +43 more
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Computing Bayes Factors Using Thermodynamic Integration [PDF]
Systematic Biology, 2006 In the Bayesian paradigm, a common method for comparing two models is to compute the Bayes factor, defined as the ratio of their respective marginal likelihoods. In recent phylogenetic works, the numerical evaluation of marginal likelihoods has often been performed using the harmonic mean estimation procedure.
Lartillot, Nicolas, Philippe, Hervé
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Consistency of Bayes factor for nonnested model selection when the model dimension grows
, 2016 Zellner's $g$-prior is a popular prior choice for the model selection problems in the context of normal regression models. Wang and Sun [J. Statist. Plann.
Maruyama, Yuzo, Wang, Min
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Asymptotic Properties of Bayes Risk of a General Class of Shrinkage Priors in Multiple Hypothesis Testing Under Sparsity [PDF]
, 2015 Consider the problem of simultaneous testing for the means of independent normal observations. In this paper, we study some asymptotic optimality properties of certain multiple testing rules induced by a general class of one-group shrinkage priors in a ...
Chakrabarti, Arijit +3 more
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Statistical parametric mapping of biomechanical one-dimensional data with Bayesian inference
International Biomechanics, 2019 Recent developments in Statistical Parametric Mapping (SPM) for continuum data (e.g. kinematic time series) have been adopted by the biomechanics research community with great interest. The Python/MATLAB package spm1d developed by T.
Ben Serrien +2 more
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Statistical Significance Testing for Mixed Priors: A Combined Bayesian and Frequentist Analysis
Entropy, 2022 In many hypothesis testing applications, we have mixed priors, with well-motivated informative priors for some parameters but not for others. The Bayesian methodology uses the Bayes factor and is helpful for the informative priors, as it incorporates ...
Jakob Robnik, Uroš Seljak
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Neyman–Pearson lemma for Bayes factors [PDF]
Communications in Statistics - Theory and Methods, 2021 8 ...
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Bayes factors for detection of Quantitative Trait Loci
Genetics Selection Evolution, 2001 A fundamental issue in quantitative trait locus (QTL) mapping is to determine the plausibility of the presence of a QTL at a given genome location. Bayesian analysis offers an attractive way of testing alternative models (here, QTL vs.
Pérez-Enciso Miguel +2 more
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Default Priors in a Zero-Inflated Poisson Distribution: Intrinsic Versus Integral Priors
MathematicsPrior elicitation is an important issue in both subjective and objective Bayesian frameworks, where prior distributions impose certain information on parameters before data are observed.
Junhyeok Hong, Kipum Kim, Seong W. Kim
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Consistency of objective Bayes factors as the model dimension grows
, 2010 In the class of normal regression models with a finite number of regressors, and for a wide class of prior distributions, a Bayesian model selection procedure based on the Bayes factor is consistent [Casella and Moreno J. Amer. Statist. Assoc. 104 (2009)
Casella, George +2 more
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