state-space models

Monte Carlo based Inference for State-space Models

Sumeetpal Singh, Reader, Engineering Statistics, Department of Engineering, University of Cambridge

Jan 31, 12:00 - 13:00

KAUST

Monte Carlo state-space models

State-space models are widely used for the analysis of time-series data but full Bayesian inference is still elusive. I will review some applications of state-space models and recent endeavours towards efficient Monte Carlo sampling, in particular using Particle filtering and more recent Particle Markov Chain Monte Carlo methods. I will discuss the theoretical scalability of these methods with respect to the length of the observed time-series. Our theoretical results on their efficiency align well with many documented instances of their effectiveness based on extensive numerical studies. The talk will conclude with some open challenges to be pursued.