2dffbc474aa176b6dc957938c15d0c8b-Reviews.html

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper presents a Bayesian approach to state and parameter estimation in nonlinear state-space models, while also learning the transition dynamics through the use of a Gaussian process (GP) prior. The inference mechanism is based on particle Markov chain Monte Carlo (PMCMC) with the recently-introduced idea of ancestor sampling. The paper also discusses computational efficiencies to be had with respect to sparsity and low-rank Cholesky updates. This is a technically sound and strong paper with clear and accessible presentation.