Derives closed-form solutions including scalar inverse-gradient density formulas for f-divergence, Bregman, and Rényi penalized variational problems at the measure level.
and Madigan, David and Raftery, Adrian E
9 Pith papers cite this work. Polarity classification is still indexing.
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A trans-dimensional Bayesian model set averaging framework for 13C-MFA that averages flux estimates over uncertain network topologies using reversible jump MCMC and diffusive nested sampling.
A Bregman-divergence generalization of ELPD enables robust predictive model selection by tuning sensitivity to tail mismatch via a parameter β.
CausalSE applies SCMs and propensity score matching to reveal that causal analysis of prompt engineering on GPT-3 code generation often finds no significant effect where associational analysis suggests improvement.
Rashomon-seeded annealing repurposes Rashomon sets as warm starts for annealed importance sampling to enable full posterior inference in factorial designs without exhaustive enumeration.
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.
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.
ShrinkageTrees is an R package implementing regularized Bayesian tree ensembles for survival outcomes and causal inference via AFT models, including the first Horseshoe Forest implementation.
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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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.