Evaluating probabilistic programming and fast variational Bayesian inference in phylogenetics

Automated Variational Inference in Probabilistic Programming

We present a new algorithm for approximate inference in probabilistic programs, based on a stochastic gradient for variational programs. This method is efficient without restrictions on the probabilistic program; it is particularly practical for distributions which are not analytically tractable, including highly structured distributions that arise in probabilistic programs. We show how to auto...

Bayesian Inference in Molecular Phylogenetics

"o. ~ ,1:. 200<;. 1--. \\.B~~ ~ ,~ ~~c..("" ." V I P '7\rt~ . :r~ M~l(.$ 4 ~~oV\ "",cJ.f"-1'~~'/ (~~. O. Gaswd) . O'6~ ~"'\v. tp,~ss.,~ I (Jf. b~O~ [49] Swofford, D., Olsen, G.J., Waddell, P.J., and Hillis, D.M. (1996). Phylogenetic inference. In Molecular Systematics (2nd edn) (ed. D. Hillis, C. Moritz, and B. Mable), pp. 438-514. Sinauer, Sutherland, MA. [50] Thorne, J.L., Goldman, N., and Jo...

Variational Inference in Probabilistic Models

Stan: A probabilistic programming language for Bayesian inference and optimization∗

Abstract Stan is a free and open-source C++ program that performs Bayesian inference or optimization for arbitrary user-specified models and can be called from the command line, R, Python, Matlab, or Julia, and has great promise for fitting large and complex statistical models in many areas of application. We discuss Stan from users’ and developers’ perspective and illustrate with a simple but ...

Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature

We propose a novel sampling framework for inference in probabilistic models: an active learning approach that converges more quickly (in wall-clock time) than Markov chain Monte Carlo (MCMC) benchmarks. The central challenge in probabilistic inference is numerical integration, to average over ensembles of models or unknown (hyper-)parameters (for example to compute the marginal likelihood or a ...