1 Preface.- 2 Introduction to state-space models.- 3 Beyond state-space models.- 4 Introduction to Markov processes.- 5 Feynman-Kac models: definition, properties and recursions.- 6 Finite state-spaces and hidden Markov models.- 7 Linear-Gaussian state-space models.- 8 Importance sampling.- 9 Importance resampling.- 10 Particle filtering.- 11 Convergence and stability of particle filters.- 12 Particle smoothing.- 13 Sequential quasi-Monte Carlo.- 14 Maximum likelihood estimation of state-space models.- 15 Markov chain Monte Carlo.- 16 Bayesian estimation of state-space models and particle MCMC.- 17 SMC samplers.- 18 SMC2, sequential inference in state-space models.- 19 Advanced topics and open problems.
About the Author: Omiros Papaspiliopoulos (PhD, Lancaster University, 2003) is an ICREA Research Professor and Director of the Data Science Center at Barcelona Graduate School of Economics. Previous positions include Full Professor at Universitat Pompeu Fabra, Assistant Professor at Warwick University and Research Associate at Lancaster and Oxford University.
He is currently co-editor of Biometrika, and has been an Associate Editor for the Journal of the Royal Statistical Society Series B, Biometrika, Journal of Uncertainty Quantification (SIAM) and Statistics and Computing. He has delivered more than 100 invited talks, and has given courses at ENSAE in Paris, the Berlin Mathematical School, the Department of Mathematics at the University of Copenhagen, and the Engineering Department at Osaka University. In 2010 he was awarded the Royal Statistical Society's Guy Medal in Bronze.
His research interests include computational statistics, applied mathematics and machine learning.
Nicolas Chopin (PhD, Université Pierre et Marie Curie, Paris, 2003) has been a Professor of Statistics at ENSAE, Paris, since 2006. He was previously a lecturer at Bristol University (UK).
He is a current or former associate editor for Annals of Statistics, Biometrika, Journal of the Royal Statistical Society, Statistics and Computing, and Statistical Methods & Applications. He has served as a member (2013-14) and secretary (2015-16) of the research section committee of the Royal Statistical Society. He received a Savage Award for his doctoral dissertation in 2002.
His research interests include computational statistics, Bayesian inference, and machine learning..
