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 …

Temperature and cell aging are two major factors that influence the reliability and safety of Li-
ion batteries. A general battery model considering both temperature and degradation is …

This paper develops and illustrates a new maximum-likelihood based method for the
identification of Hammerstein–Wiener model structures. A central aspect is that a very …

The main content of this review article is first to review the main inference tools using Bayes
rule, the maximum entropy principle (MEP), information theory, relative entropy and the …

Bayesian methods have become very popular in signal processing lately, even though
performing exact Bayesian inference is often unfeasible due to the lack of analytical …

This article provides an overview of nonlinear statistical signal processing based on the
Bayesian paradigm. The next-generation processors are well founded on MC simulation …

Monte Carlo (MC) methods are well-known computational techniques, widely used in
different fields such as signal processing, communications and machine learning. An …

Particle filtering is a sequential signal processing methodology that uses discrete random
measures composed of particles and weights to approximate probability distributions of …

Two new recursive approaches have been developed to provide accurate estimates for
posterior moments of both parameters and system states while making use of the …

Many signal processing applications require performing statistical inference on large
datasets, where computational and/or memory restrictions become an issue. In this big data …