A Hilbert space embedding of a distribution—in short, a kernel mean embedding—has
recently emerged as a powerful tool for machine learning and statistical inference. The basic …

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 …

Machine learning develops rapidly, which has made many theoretical breakthroughs and is
widely applied in various fields. Optimization, as an important part of machine learning, has …

Now in its second edition, this accessible text presents a unified Bayesian treatment of state-
of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state …

Fully Bayesian multispecies coalescent (MSC) methods like* BEAST estimate species trees
from multiple sequence alignments. Today thousands of genes can be sequenced for a …

Uncertainty quantification serves a central role for simulation-based analysis of physical,
engineering, and biological applications using mechanistic models. From a broad …

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 …

We present a newly developed Multivariate Copula Analysis Toolbox (MvCAT) which
includes a wide range of copula families with different levels of complexity. MvCAT employs …

Radiocarbon dating is routinely used in paleoecology to build chronologies of lake and peat
sediments, aiming at inferring a model that would relate the sediment depth with its age. We …

Hamiltonian dynamics can be used to produce distant proposals for the Metropolis
algorithm, thereby avoiding the slow exploration of the state space that results from the …