Bayesian Model Selection
PulseAugur coverage of Bayesian Model Selection — every cluster mentioning Bayesian Model Selection across labs, papers, and developer communities, ranked by signal.
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New two-step Metropolis-Hastings algorithm enhances Bayesian empirical likelihood methods
Researchers have developed a novel two-step Metropolis-Hastings algorithm designed to improve the efficiency of Markov chain Monte Carlo (MCMC) sampling for Bayesian empirical likelihood (BayesEL) methods. This new appr…
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New Bayesian Wind Tunnels method enables transformers for model selection
Researchers have developed a novel method called Bayesian Wind Tunnels to enable transformers to perform Bayesian model selection, identifying the correct hypothesis class from data. Using fixed-point-free involutions, …
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New AI methods tackle complex inverse problems with improved sampling
Researchers are developing new methods to tackle complex inverse problems in machine learning, particularly in scenarios where gradient information is unavailable. New techniques aim to improve sampling from high-dimens…
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Bayesian model selection via ELBO can overfit, cautioning practitioners
A new paper explores the relationship between the Evidence Lower Bound (ELBO) and Occam's Razor in Bayesian model selection. The research demonstrates that ELBO-based hyperparameter learning can lead to overfitting, con…