Nonlinear non-Gaussian state-space models are ubiquitous in statistics, econometrics,
information engineering and signal processing. Particle methods, also known as Sequential …

Diffusion models have been successful on a range of conditional generation tasks including
molecular design and text-to-image generation. However, these achievements have …

This work offers a broad perspective on probabilistic modeling and inference in light of
recent advances in probabilistic programming, in which models are formally expressed in …

We present an offline, iterated particle filter to facilitate statistical inference in general state
space hidden Markov models. Given a model and a sequence of observations, the …

Sequential latent variable models (SLVMs) are essential tools in statistics and machine
learning, with applications ranging from healthcare to neuroscience. As their flexibility …

We introduce a general form of sequential Monte Carlo algorithm defined in terms of a
parameterized resampling mechanism. We find that a suitably generalized notion of the …

The identification of nonlinear systems is a challenging problem. Physical knowledge of a
system can be used in the identification process to significantly improve the predictive …

Feynman-Kac path integration models arise in a large variety of scientic disciplines
including physics, chemistry and signal processing. Their mean eld particle interpretations …

Parameter learning for high-dimensional, partially observed, and nonlinear stochastic
processes is a methodological challenge. Spatiotemporal disease transmission systems …

Abstract Sequential Monte Carlo (SMC) is an inference algorithm for state space models that
approximates the posterior by sampling from a sequence of target distributions. The target …