Statistical signal processing applications usually require the estimation of some parameters
of interest given a set of observed data. These estimates are typically obtained either by …

This paper surveys various results about Markov chains on general (non-countable) state
spaces. It begins with an introduction to Markov chain Monte Carlo (MCMC) algorithms …

Heuristically, a discrete-time stochastic process has the Markov property if the past and
future are independent given the present. In this introductory chapter, we give the formal …

We study the problem of sampling from a strongly log-concave density supported on
$\mathbb {R}^ d $, and prove a non-asymptotic upper bound on the mixing time of the …

Optimization algorithms and Monte Carlo sampling algorithms have provided the
computational foundations for the rapid growth in applications of statistical machine learning …

Structured additive regression models are perhaps the most commonly used class of models
in statistical applications. It includes, among others,(generalized) linear …

General state-space Markov chain theory has evolved to make it both more accessible and
more powerful. Markov Chain Monte Carlo in Practice introduces MCMC methods and their …

We provide a detailed, introductory exposition of the Metropolis-Hastings algorithm, a
powerful Markov chain method to simulate multivariate distributions. A simple, intuitive …

Markov Chains and Stochastic Stability is part of the Communications and Control
Engineering Series (CCES) edited by Professors BW Dickinson, ED Sontag, M. Thoma, A …

This purpose of this introductory paper is threefold. First, it introduces the Monte Carlo
method with emphasis on probabilistic machine learning. Second, it reviews the main …