In the host of numerical schemes devised to calculate free energy differences by way of
geometric transformations, the adaptive biasing force algorithm has emerged as a promising …

Recent decades have seen enormous improvements in computational inference for
statistical models; there have been competitive continual enhancements in a wide range of …

Stochastic approximation (SA) is a key method used in statistical learning. Recently, its non-
asymptotic convergence analysis has been considered in many papers. However, most of …

In this paper, a real-time composite fatigue life prognosis framework is proposed. The
proposed methodology combines Bayesian inference, piezoelectric sensor measurements …

This paper presents a new crack size quantification method based on in-situ Lamb wave
testing and Bayesian method. The proposed method uses coupon test to develop a baseline …

We study a version of the proximal gradient algorithm for which the gradient is intractable
and is approximated by Monte Carlo methods (and in particular Markov Chain Monte Carlo) …

Supplement to “A framework for adaptive MCMC targeting multimodal distributions”. In
Supplementary Material A we present the proofs of our theoretical results of Section 3 and …

We construct a new framework for accelerating Markov chain Monte Carlo in posterior
sampling problems where standard methods are limited by the computational cost of the …

We raise the issue of estimating the division rate for a growing and dividing population
modelled by a piecewise deterministic Markov branching tree. Such models have broad …

Parallel tempering is a generic Markov chain Monte Carlo sampling method which allows
good mixing with multimodal target distributions, where conventional Metropolis-Hastings …