ABSTRACT Statisticians often use Monte Carlo methods to approximate probability
distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential …

Experimental solid mechanics is at a pivotal point where machine learning (ML) approaches
are rapidly proliferating into the discovery process due to significant advances in data …

The paper is aimed at develo** an efficient and robust probabilistic fatigue life prediction
framework for adhesively bonded joints. This framework calibrates the fatigue life model by …

The particle degradation problem of particle filter (PF) algorithm caused by reduction of
particle weights significantly influences the positioning accuracy of target nodes in wireless …

Bayesian inference with sequential Monte Carlo is used to quantify the orientation
distribution function coefficients and to calculate the fully anisotropic elastic constants of …

Antimicrobial resistance (AMR) emerges when disease-causing microorganisms develop
the ability to withstand the effects of antimicrobial therapy. This phenomenon is often fueled …

Sequential Monte Carlo Samplers with Independent Markov Chain Monte Carlo Proposals Page
1 Bayesian Analysis (2019) 14, Number 3, pp. 753–776 Sequential Monte Carlo Samplers with …

Robust pedestrian trajectory-tracking is an essential prerequisite to traffic accident
prevention. However, it is a challenging task in urban autonomous driving, since the weak …

Abstract Bayesian inference with Sequential Monte Carlo was used to determine the single
crystal elastic constants of additively manufactured (AM) cobalt‑nickel-based superalloy …

By facilitating the generation of samples from arbitrary probability distributions, Markov
Chain Monte Carlo (MCMC) is, arguably, the tool for the evaluation of Bayesian inference …