The paper proposes Metropolis adjusted Langevin and Hamiltonian Monte Carlo sampling
methods defined on the Riemann manifold to resolve the shortcomings of existing Monte …

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 …

Dynamic stochastic general equilibrium (DSGE) models have become one of the
workhorses of modern macroeconomics and are extensively used for academic research as …

In this paper, we develop a Bayesian framework to estimate a proxy structural vector
autoregression to identify monetary policy shocks. We find that during the Great Moderation …

Multitarget tracking (MTT) refers to the problem of jointly estimating the number of targets
and their states or trajectories from noisy sensor measurements. MTT has a long history …

Summary Markov chain Monte Carlo and sequential Monte Carlo methods have emerged as
the two main tools to sample from high dimensional probability distributions. Although …

Nonlinear non-Gaussian state-space models are ubiquitous in statistics, econometrics,
information engineering and signal processing. Particle methods, also known as Sequential …

This chapter provides an overview of solution and estimation techniques for dynamic
stochastic general equilibrium models. We cover the foundations of numerical …

We propose a methodology to sample sequentially from a sequence of probability
distributions that are defined on a common space, each distribution being known up to a …

Bayesian experimental design is a fast growing area of research with many real‐world
applications. As computational power has increased over the years, so has the development …