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.
and Madigan, David and Raftery, Adrian E
3 Pith papers cite this work. Polarity classification is still indexing.
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Rectified AI priors, obtained by correcting AI-induced data laws before embedding them in techniques like Dirichlet process priors, reduce bias, improve credible interval coverage, and boost performance in tasks like skin disease classification.
Relative plausibility theory supplies a computational-level account of comparing explanations against evidence in legal proof, while probabilistic methods supply algorithmic-level implementations, and the two correspond when plausibility judgments meet basic coherence conditions.
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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.
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Supercharging Bayesian Inference with Reliable AI-Informed Priors
Rectified AI priors, obtained by correcting AI-induced data laws before embedding them in techniques like Dirichlet process priors, reduce bias, improve credible interval coverage, and boost performance in tasks like skin disease classification.
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Relative plausibility versus probabilism: A level-of-analysis error in juridical proof
Relative plausibility theory supplies a computational-level account of comparing explanations against evidence in legal proof, while probabilistic methods supply algorithmic-level implementations, and the two correspond when plausibility judgments meet basic coherence conditions.