Formalizes pre-data effective sample size for GGMs under Wishart and G-Wishart priors and introduces DPIR and BFDA extensions for sample size planning.
and Berger, James O
2 Pith papers cite this work. Polarity classification is still indexing.
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Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.
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What is your Prior Worth? Effective Sample Size and Sample Size Planning for Gaussian Graphical Models
Formalizes pre-data effective sample size for GGMs under Wishart and G-Wishart priors and introduces DPIR and BFDA extensions for sample size planning.
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Reliable model selection in the presence of parameter non-identifiability
Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.