Particle filters are of great concern in a large variety of engineering fields such as robotics,
statistics or automatics. Recently, it has developed among Prognostics and Health …

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

Nonlinear system identification is an extremely broad topic, since every system that is not
linear is nonlinear. That makes it impossible to give a full overview of all aspects of the fi eld …

Dynamic stochastic general equilibrium (DSGE) models have become one of the
workhorses of modern macroeconomics and are extensively used for academic research as …

Since the first edition of Stochastic Modelling for Systems Biology, there have been many
interesting developments in the use of" likelihood-free" methods of Bayesian inference for …

This chapter provides an overview of solution and estimation techniques for dynamic
stochastic general equilibrium models. We cover the foundations of numerical …

When used as a surrogate objective for maximum likelihood estimation in latent variable
models, the evidence lower bound (ELBO) produces state-of-the-art results. Inspired by this …

This paper presents a Matlab-based tutorial for model-based prognostics, which combines a
physical model with observed data to identify model parameters, from which the remaining …

Abstract Particle Markov chain Monte Carlo (PMCMC) is a systematic way of combining the
two main tools used for Monte Carlo statistical inference: sequential Monte Carlo (SMC) and …

When an unbiased estimator of the likelihood is used within a Metropolis–Hastings chain, it
is necessary to trade off the number of Monte Carlo samples used to construct this estimator …