Progress in defining genomic fitness landscapes in cancer, especially those defined by copy
number alterations (CNAs), has been impeded by lack of time-series single-cell sampling of …

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 …

Recently, there have been conceptually new developments in Monte Carlo methods through
the introduction of new MCMC and sequential Monte Carlo (SMC) algorithms which are …

We derive and study sequential quasi Monte Carlo (SQMC), a class of algorithms obtained
by introducing QMC point sets in particle filtering. SQMC is related to, and may be seen as …

Abstract The Wright–Fisher family of diffusion processes is a widely used class of
evolutionary models. However, simulation is difficult because there is no known closed-form …

In this paper we propose a new theory and methodology to tackle the problem of unifying
Monte Carlo samples from distributed densities into a single Monte Carlo draw from the …

Combining several (sample approximations of) distributions, which we term sub-posteriors,
into a single distribution proportional to their product, is a common challenge. Occurring, for …

Consider a multidimensional diffusion process X={X(t):t∈0,1\}. Let ε>0 be a deterministic,
user defined, tolerance error parameter. Under standard regularity conditions on the drift …

We consider the problem of statistical inference for a class of partially-observed diffusion
processes, with discretely-observed data and finite-dimensional parameters. We construct …

This paper introduces a class of Monte Carlo algorithms which are based upon simulating a
Markov process whose quasi-stationary distribution coincides with the distribution of interest …