The Bayesian information criterion (BIC) is one of the most widely known and pervasively
used tools in statistical model selection. Its popularity is derived from its computational …

Bayesian Model Selection and Model Averaging Page 1 Journal of Mathematical
Psychology 44, 92 107 (2000) Bayesian Model Selection and Model Averaging Larry …

A concise and self-contained introduction to causal inference, increasingly important in data
science and machine learning. The mathematization of causality is a relatively recent …

Cluster analysis finds groups in data automatically. Most methods have been heuristic and
leave open such central questions as: how many clusters are there? Which method should I …

Discovering causal structure among a set of variables is a fundamental problem in many
empirical sciences. Traditional score-based casual discovery methods rely on various local …

We consider the problem of learning causal directed acyclic graphs from an observational
joint distribution. One can use these graphs to predict the outcome of interventional …

In a 1935 paper and in his book Theory of Probability, Jeffreys developed a methodology for
quantifying the evidence in favor of a scientific theory. The centerpiece was a number, now …

Cluster analysis is the automated search for groups of related observations in a dataset.
Most clustering done in practice is based largely on heuristic but intuitively reasonable …

Hidden Markov models—most often abbreviated to the acronym “HMMs”—are one of the
most successful statistical modelling ideas that have came up in the last forty years: the use …

Third Edition brings the text up to date with new material and updated references. New
content includes an introduction to left and interval censored data; the log-logistic …