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 …

One of the common goals of time series analysis is to use the observed series to inform
predictions for future observations. In the absence of any actual new data to predict, cross …

Actions description languages (ADLs), such as STRIPS, PDDL, and RDDL specify the input
format for planning algorithms. Unfortunately, their syntax is familiar to planning experts only …

Metabarcoding (high‐throughput sequencing of marker gene amplicons) has emerged as a
promising and cost‐effective method for characterizing insect community samples. Yet, the …

This paper introduces SMCP3, a method for automatically implementing custom sequential
Monte Carlo samplers for inference in probabilistic programs. Unlike particle filters and …

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

Probability theory and statistics are fundamental disciplines in a data-driven world. Synthetic
probability theory is a general, axiomatic formalism to describe their underlying structures …

We show that streams and lazy data structures are a natural idiom for programming with
infinite-dimensional Bayesian methods such as Poisson processes, Gaussian processes …

Software tools for Bayesian inference have undergone rapid evolution in the past three
decades, following popularisation of the first generation MCMC-sampler implementations …