Alexander M. G. Cox

Reducing Complexity of Pseudo-Marginal Sequential Monte Carlo via Hierarchical Tempering

The Bayesian approach to inverse problems provides a powerful framework for inferring unknown parameters from noisy observations. Among the computationally most challenging statistical inference problems are problems with intractable likelihoods, especially if no unbiased likelihood estimator is available. We focus on...