Markov chain Monte Carlo algorithms are used to simulate from complex statistical
distributions by way of a local exploration of these distributions. This local feature avoids …

Recent decades have seen enormous improvements in computational inference for
statistical models; there have been competitive continual enhancements in a wide range of …

Markov chain Monte Carlo (MCMC) is a popular and successful general-purpose tool for
Bayesian inference. However, MCMC cannot be practically applied to large data sets …

Datasets are growing not just in size but in complexity, creating a demand for rich models
and quantification of uncertainty. Bayesian methods are an excellent fit for this demand, but …

The explosive growth in data volume and the availability of cheap computing resources
have sparked increasing interest in Big learning, an emerging subfield that studies scalable …

This thesis presents five contributions to machine learning, with themes of differentiability
and Bayesian inference. We present Firefly Monte Carlo, an auxiliary variable Markov chain …

MultiBUGS is a new version of the general-purpose Bayesian modelling software BUGS that
implements a generic algorithm for parallelising Markov chain Monte Carlo (MCMC) …

MCMC algorithms such as Metropolis-Hastings algorithms are slowed down by the
computation of complex target distributions as exemplified by huge datasets. We offer in this …

Probabilistic models (PMs) are ubiquitously used across a variety of machine learning
applications. They have been shown to successfully integrate structural prior information …

Geostatistical inversion with quantified uncertainty for nonlinear problems requires
techniques for providing conditional realizations of the random field of interest. Many first …