Stochastic fluctuations of molecule numbers are ubiquitous in biological systems. Important
examples include gene expression and enzymatic processes in living cells. Such systems …

We provide the first complete continuous time framework for denoising diffusion models of
discrete data. This is achieved by formulating the forward noising process and …

There has been a resurgence of interest in non-equilibrium stochastic processes in recent
years, driven in part by the observation that the number of molecules (genes, mRNA …

The first comprehensive graduate-level introduction to stochastic thermodynamics
Stochastic thermodynamics is a well-defined subfield of statistical physics that aims to …

Since the first edition of Stochastic Modelling for Systems Biology, there have been many
interesting developments in the use of" likelihood-free" methods of Bayesian inference for …

One of the major challenges in modern biology is to understand how the molecular
components of a living cell operate in a highly noisy environment. What are the specific …

The Zig-Zag process and super-efficient sampling for Bayesian analysis of big data Page 1 The
Annals of Statistics 2019, Vol. 47, No. 3, 1288–1320 https://doi.org/10.1214/18-AOS1715 © …

This book, as with others in the series, is intended to provide supplementary material for
courses in probability or stochastic processes. The mathematical focus is on counting …

We outline our perspective on stochastic chemical kinetics, paying particular attention to
numerical simulation algorithms. We first focus on dilute, well-mixed systems, whose …

A reaction network is a chemical system involving multiple reactions and chemical species.
The simplest stochastic models of such networks treat the system as a continuous time …