Publisher Summary Mixture distributions comprise a finite or infinite number of components,
possibly of different distributional types, that can describe different features of data. The …

Markov chain Monte Carlo algorithms are used to simulate from complex statistical
distributions by way of a local exploration of these distributions. This local feature avoids …

Pre-trained diffusion models have been successfully used as priors in a variety of linear
inverse problems, where the goal is to reconstruct a signal from noisy linear measurements …

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 …

General state-space Markov chain theory has evolved to make it both more accessible and
more powerful. Markov Chain Monte Carlo in Practice introduces MCMC methods and their …

Provides statistical tools and techniques needed to understand today's financial markets The
Second Edition of this critically acclaimed text provides a comprehensive and systematic …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

Among the many uses of hierarchical modeling, their application to the statistical analysis of
spatial and spatio-temporal data from areas such as epidemiology And environmental …

1.1 Introduction Research into time series models of changing variance and covariance,
which I will collectively call volatility models, has exploded in the last ten years. This activity …

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 …