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 …

Markov chain Monte Carlo–based Bayesian data analysis has now become the method of
choice for analyzing and interpreting data in almost all disciplines of science. In astronomy …

A general framework for constructing and using probabilistic models of complex systems that
would enable a computer to use available information for making decisions. Most tasks …

Many tasks involve predicting a large number of variables that depend on each other as well
as on other observed variables. Structured prediction methods are essentially a combination …

Autonomous agents optimize the reward function we give them. What they don't know is how
hard it is for us to design a reward function that actually captures what we want. When …

This chapter demonstrates how the Ising model can be estimated. It shows that the Ising
model is equivalent to, or closely related to, prominent modeling techniques in …

Many modern statistical applications involve inference for complex stochastic models, where
it is easy to simulate from the models, but impossible to calculate likelihoods. Approximate …

In recent years, network models have been proposed as an alternative representation of
psychometric constructs such as depression. In such models, the covariance between …

This book is a graduate-level introduction to probabilistic programming. It not only provides a
thorough background for anyone wishing to use a probabilistic programming system, but …

State-of-the-art research on MRFs, successful MRF applications, and advanced topics for
future study. This volume demonstrates the power of the Markov random field (MRF) in …