Statistical signal processing applications usually require the estimation of some parameters
of interest given a set of observed data. These estimates are typically obtained either by …

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

We advocate an optimization-centric view of Bayesian inference. Our inspiration is the
representation of Bayes' rule as infinite-dimensional optimization (Csisz´ r, 1975; Donsker …

Abstract Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) algorithm
that avoids the random walk behavior and sensitivity to correlated parameters that plague …

Many of the most exciting problems in applied statistics involve intricate, typically high-
dimensional, models and, at least relative to the model complexity, sparse data. With the …

Many problems arising in applications result in the need to probe a probability distribution
for functions. Examples include Bayesian nonparametric statistics and conditioned diffusion …

Abstract Although Hamiltonian Monte Carlo has proven an empirical success, the lack of a
rigorous theoretical understanding of the algorithm has in many ways impeded both …

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

Modern signal processing (SP) methods rely very heavily on probability and statistics to
solve challenging SP problems. SP methods are now expected to deal with ever more …

Supplement to “On the geometric ergodicity of Hamiltonian Monte Carlo”. We provide
additional examples of π-irreducibility, with supporting plots, as well as elaborating on the …