Many scientific fields have experienced growth in the use of stochastic differential equations
(SDEs), also known as diffusion processes, to model scientific phenomena over time. SDEs …

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 …

This book is a graduate-level introduction to probabilistic programming. It not only provides a
thorough background for anyone wishing to use a probabilistic programming system, but …

Abstract Machine learning (ML) holds great potential for accurately forecasting treatment
outcomes over time, which could ultimately enable the adoption of more individualized …

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 consider the problem of simulating diffusion bridges, which are diffusion processes that
are conditioned to initialize and terminate at two given states. The simulation of diffusion …

Parameter inference for stochastic differential equations is challenging due to the presence
of a latent diffusion process. Working with an Euler-Maruyama discretisation for the diffusion …

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 …

In state–space models, smoothing refers to the task of estimating a latent stochastic process
given noisy measurements related to the process. We propose an unbiased estimator of …

We introduce a class of generative network models that insert edges by connecting the
starting and terminal vertices of a random walk on the network graph. Within the taxonomy of …