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 is an up-to-date introduction to, and overview of, marginal likelihood computation for
model selection and hypothesis testing. Computing normalizing constants of probability …

Abstract The Effective Sample Size (ESS) is an important measure of efficiency of Monte
Carlo methods such as Markov Chain Monte Carlo (MCMC) and Importance Sampling (IS) …

General multivariate distributions are notoriously expensive to sample from, particularly the
high-dimensional posterior distributions in PDE-constrained inverse problems. This paper …

Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in
statistics, signal processing and machine learning. A well-known class of MC methods are …

Bayesian approaches provide a principled solution to the exploration-exploitation trade-off
in Reinforcement Learning. Typical approaches, however, either assume a fully observable …

The current work focuses on ranked set sampling and a simple random sample as sampling
approaches for determining stress–strength reliability from the inverted Topp–Leone …

A random field is the representation of the joint probability distribution for a set of random
variables. Markov fields, in particular, have a long standing tradition as the theoretical …

Inference of fuzzy reliability model for inverse Rayleigh distribution Page 1 AIMS
Mathematics, 6(9): 9770–9785. DOI: 10.3934/math.2021568 Received: 29 March 2021 …

Abstract The use of Cauchy Markov random field priors in statistical inverse problems can
potentially lead to posterior distributions which are non-Gaussian, high-dimensional …