Bayesian Inference: Bayes theorem, prior, posterior and predictive distributions, conjugate models (Normal-Normal, Poisson-Gamma, Beta-Binomial), Bayesian point estimation, credible intervals and ...

Approximate Bayesian inferences based on MAP, Evidence, Asymptotic, VB, and MCMC approximations are provided as well as full derivations of calculations, useful notations, formulas, and rules. The ...

We develop new methods and algorithms for coping with uncertainty in artificial intelligence, focusing in particular on approximate Bayesian inference of probabilistic programs. We also solve ...

We study brains and computers alike as statistical inference engines which are probabilistic, approximate, active, robust, and resource-constrained. We develop new methods for approximate Bayesian ...

Bayesianism is enjoying a revival across many fields, and it offers a powerful tool for improving inference and analytic transparency in qualitative research. This course introduces basic principles ...

Bayesian regression analysis enables researchers to predict a range of outcomes instead of a single estimate ... Kowal et al, Monte Carlo Inference for Semiparametric Bayesian Regression, Journal ...