The Bayesian statistical paradigm provides a principled and coherent approach to
probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting …

We propose subsampling Markov chain Monte Carlo (MCMC), an MCMC framework where
the likelihood function for n observations is estimated from a random subset of m …

This chapter surveys progress on three related topics in perturbations of Markov chains: the
motivating question of when and how" perturbed" MCMC chains are developed, the …

The Bayesian model updating approach has attracted much attention by providing the most
probable values (MPVs) of physical parameters and their uncertainties. However, the …

This paper takes the reader on a journey through the history of Bayesian computation, from
the 18th century to the present day. Beginning with the one-dimensional integral first …

The 21st century has seen an enormous growth in the development and use of approximate
Bayesian methods. Such methods produce computational solutions to certain “intractable” …

Damage detection inevitably involves uncertainties originated from measurement noise and
modeling error. It may cause incorrect damage detection results if not appropriately treating …

Abstract Hamiltonian Monte Carlo (HMC) samples efficiently from high-dimensional
posterior distributions with proposed parameter draws obtained by iterating on a discretized …

When conducting Bayesian inference, delayed-acceptance (DA) Metropolis–Hastings (MH)
algorithms and DA pseudo-marginal MH algorithms can be applied when it is …

We describe an “embarrassingly parallel” method for Bayesian phylogenetic inference,
annealed Sequential Monte Carlo (SMC), based on recent advances in the SMC literature …