Sequential Monte Carlo methods—also known as particle filters—offer approximate
solutions to filtering problems for nonlinear state-space systems. These filtering problems …

ABSTRACT Statisticians often use Monte Carlo methods to approximate probability
distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential …

When used as a surrogate objective for maximum likelihood estimation in latent variable
models, the evidence lower bound (ELBO) produces state-of-the-art results. Inspired by this …

A core problem in statistics and probabilistic machine learning is to compute probability
distributions and expectations. This is the fundamental problem of Bayesian statistics and …

Modern parallel computing devices, such as the graphics processing unit (GPU), have
gained significant traction in scientific and statistical computing. They are particularly well …

We introduce a new approach for amortizing inference in directed graphical models by
learning heuristic approximations to stochastic inverses, designed specifically for use as …

One of the key challenges in identifying nonlinear and possibly non-Gaussian state space
models (SSMs) is the intractability of estimating the system state. Sequential Monte Carlo …

This paper presents a new approach to automatically discovering accurate models of
complex time series data. Working within a Bayesian nonparametric prior over a symbolic …

We propose nested sequential Monte Carlo (NSMC), a methodology to sample from
sequences of probability distributions, even where the random variables are high …

Statistical phylogenetic analysis currently relies on complex, dedicated software packages,
making it difficult for evolutionary biologists to explore new models and inference strategies …