This tutorial paper reviews the use of advanced Monte Carlo sampling methods in the
context of Bayesian model updating for engineering applications. Markov Chain Monte …

We review adaptive Markov chain Monte Carlo algorithms (MCMC) as a mean to optimise
their performance. Using simple toy examples we review their theoretical underpinnings …

Bayesian inference has found widespread application and use in science and engineering
to reconcile Earth system models with data, including prediction in space (interpolation) …

Subset Simulation is an adaptive simulation method that efficiently solves structural
reliability problems with many random variables. The method requires sampling from …

We propose to combine two quite powerful ideas that have recently appeared in the Markov
chain Monte Carlo literature: adaptive Metropolis samplers and delayed rejection. The …

Meaningful quantification of data and structural uncertainties in conceptual rainfall‐runoff
modeling is a major scientific and engineering challenge. This paper focuses on the total …

Abstract Markov chain Monte Carlo (MCMC) methods have found widespread use in many
fields of study to estimate the average properties of complex systems, and for posterior …

Spatially distributed hydrologic models are increasingly being used to study and predict soil
moisture flow, groundwater recharge, surface runoff, and river discharge. The usefulness …

The Metropolis–Hastings algorithm (Metropolis et al., 1953; Hastings, 1970) requires choice
of proposal distributions, and it is well known that some proposals work much better than …

A greedy algorithm for the construction of a reduced model with reduction in both parameter
and state is developed for an efficient solution of statistical inverse problems governed by …