Abstract Autonomous Underwater Vehicles (AUVs) have been focused on by research
efforts because of their extensive applications in scientific, commercial as well as military …

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 …

This classic textbook builds theoretical statistics from the first principles of probability theory.
Starting from the basics of probability, the authors develop the theory of statistical inference …

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 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 …

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" …

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 …

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 …