mavii

Students must have completed Mathematical Methods (MA100) and Elementary Statistical Theory ... Lopes, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference A. Gelman, Bayesian data analysis.

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 interesting practical problems across multiple application ...

The suggested augmentation robustifies the baseline parametric model to local misspecification, while preserving the appeal of Bayesian inference. We develop an ... U.S. monthly macroeconomic time series, the method is found to improve upon autoregressive ...

The ideal candidate has a strong background in computational statistics and/or machine learning, particularly in approximate Bayesian inference and/or probabilistic machine learning methods (e.g., MCMC, normalizing flows, variational inference, Bayesian ...

Bayesian Inference: Bayes theorem, prior ... Implementation: Asymptotic approximations (Laplace approximation, Variational Bayes, Monte Carlo methods), Markov Chain Monte Carlo (MCMC) simulation (Gibbs sampler, Metropolis-Hastings algorithm).