Bayesian inference for model selection centres on comparing competing hypotheses by evaluating how well each model explains observed data, accounting for prior beliefs about parameters. The ...

Variational inference is a family of optimisation-based methods for approximating complex posterior distributions in Bayesian models. By transforming inference into an optimisation problem, these ...

Google Researchers have proposed a training method that teaches large language models to approximate Bayesian reasoning by learning from the predictions of an optimal Bayesian system. The approach ...