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 …

Numerous capability and safety techniques of Large Language Models (LLMs), including
RLHF, automated red-teaming, prompt engineering, and infilling, can be cast as sampling …

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 …

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 …

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 …

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 …

This manuscript gives a big-picture, up-to-date overview of the field of (deep) reinforcement
learning and sequential decision making, covering value-based RL, policy-gradient …

Given an unconditional diffusion model $\pi (x, y) $, using it to perform conditional simulation
$\pi (x\mid y) $ is still largely an open question and is typically achieved by learning …