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

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

We describe an “embarrassingly parallel” method for Bayesian phylogenetic inference,
annealed Sequential Monte Carlo (SMC), based on recent advances in the SMC literature …

We introduce a new sequential Monte Carlo algorithm we call the particle cascade. The
particle cascade is an asynchronous, anytime alternative to traditional sequential Monte …

Modern infectious disease outbreak surveillance produces continuous streams of sequence
data which require phylogenetic analysis as data arrives. Current software packages for …

Hidden Markov models can describe time series arising in various fields of science, by
treating the data as noisy measurements of an arbitrarily complex Markov process …

We introduce an alternative to reservoir sampling, a classic and popular algorithm for
drawing a fixed-size subsample from streaming data in a single pass. Rather than draw a …

In Bayesian inference, we seek to compute information about random variables such as
moments or quantiles on the basis of available data and prior information. When the …

We develop a novel probabilistic model for graph matchings and develop practical inference
methods for supervised and unsupervised learning of the parameters of this model. The …

Monte Carlo methods have emerged as standard tools to do Bayesian statistical inference
for sophisticated models. Sequential Monte Carlo (SMC) and Markov chain Monte Carlo …