This new edition continues to serve as a comprehensive guide to modern and classical
methods of statistical computing. The book is comprised of four main parts spanning the …

Thanks to their inherent properties, probabilistic graphical models are one of the prime
candidates for machine learning and decision making tasks especially in uncertain domains …

The investigation of directed acyclic graphs (DAGs) encoding the same Markov property,
that is the same conditional independence relations of multivariate observational …

Graphical models in their modern form have been around since the late 1970s and appear
today in many areas of the sciences. Along with the ongoing developments of graphical …

We introduce graphical time series models for the analysis of dynamic relationships among
variables in multivariate time series. The modelling approach is based on the notion of …

We present a novel Markov chain Monte Carlo (MCMC) algorithm that generates samples
from transdimensional distributions encoding complex constraints. We use factor graphs, a …

We propose a definition of causality for time series in terms of the effect of an intervention in
one component of a multivariate time series on another component at some later point in …

Ordered sequences of univariate or multivariate regressions provide statistical models for
analysing data from randomized, possibly sequential interventions, from cohort or multi …

Tracking by assignment is well suited for tracking a varying number of divisible cells, but
suffers from false positive detections. We reformulate tracking by assignment as a chain …

The motivation for the paper is the geometric approach to learning Bayesian network (BN)
structure. The basic idea of our approach is to represent every BN structure by a certain …