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 …
Variational inference has become a widely used method to approximate posteriors in complex latent variables models. However, deriving a variational inference algorithm …
A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions. Most tasks …
F Gustafsson - IEEE Aerospace and Electronic Systems …, 2010 - ieeexplore.ieee.org
The particle filter (PF) was introduced in 1993 as a numerical approximation to the nonlinear Bayesian filtering problem, and there is today a rather mature theory as well as a number of …
In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time …
This book provides a self-contained and up-to-date treatment of the Monte Carlo method and develops a common framework under which various Monte Carlo techniques can be" …
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 …
The purpose of this book is to provide a self-contained entry into Monte Carlo computational techniques. First and foremost, it must not be confused with a programming addendum to …
Statistical pattern recognition is a very active area of study andresearch, which has seen many advances in recent years. New andemerging applications-such as data mining, web …