Markov chain Monte Carlo methods are often deemed too computationally intensive to be of
any practical use for big data applications, and in particular for inference on datasets …

Nonlinear non-Gaussian state-space models are ubiquitous in statistics, econometrics,
information engineering and signal processing. Particle methods, also known as Sequential …

Having the ability to work with complex models can be highly beneficial. However, complex
models often have intractable likelihoods, so methods that involve evaluation of the …

We propose subsampling Markov chain Monte Carlo (MCMC), an MCMC framework where
the likelihood function for n observations is estimated from a random subset of m …

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

Generalised Bayesian inference updates prior beliefs using a loss function, rather than a
likelihood, and can therefore be used to confer robustness against possible mis …

We examine the behaviour of the pseudo-marginal random walk Metropolis algorithm,
where evaluations of the target density for the accept/reject probability are estimated rather …

The pseudomarginal algorithm is a Metropolis–Hastings-type scheme which samples
asymptotically from a target probability density when we can only estimate unbiasedly an …

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 …

Recent advances in sensor technologies, field methodologies, numerical modeling, and
inversion approaches have contributed to unprecedented imaging of hydrogeological …