Particle Markov chain Monte Carlo (PMCMC) is a systematic way of combining the two main
tools used for Monte Carlo statistical inference: sequential Monte Carlo (SMC) and Markov …

The prognosis and health management of lithium-ion batteries are extremely important
issues for operating performance as well as the cost of energy storage systems in vehicular …

A non-parametric extension of control variates is presented. These leverage gradient
information on the sampling density to achieve substantial variance reduction. It is not …

Monte Carlo methods, in particular those based on Markov chains and on interacting particle
systems, are by now tools that are routinely used in machine learning. These methods have …

The particle Gibbs (PG) sampler was introduced in [1] as a way to incorporate a particle filter
(PF) in a Markov chain Monte Carlo (MCMC) sampler. The resulting method was shown to …

Density tempering (also called density annealing) is a sequential Monte Carlo approach to
Bayesian inference for general state models which is an alternative to Markov chain Monte …

Owing to low cost and relatively accurate range measurement, ultrasonic sensors are widely
used in various simultaneous localisation and map** (SLAM) applications. In spite of the …

This thesis develops computationally efficient methodology in two areas. Firstly, we consider
a particularly challenging class of discretely observed continuous-time point-process …

A key question in brain sciences is how to identify time-evolving functional connectivity, such
as that obtained from recordings of neuronal activity over time. We wish to explain the …

Abstract Particle Markov Chain Monte Carlo (PMCMC) is a powerful approach to Bayesian
computation for general state space models. Our article enables PMCMC to handle a larger …