This book provides a general framework for learning sparse graphical models with conditional independence tests. It includes complete treatments for Gaussian, Poisson, multinomial, and mixed data; unified treatments for covariate adjustments, data integration, and network comparison; unified treatments for missing data and heterogeneous data; efficient methods for joint estimation of multiple graphical models; effective methods of high-dimensional variable selection; and effective methods of high-dimensional inference. The methods possess an embarrassingly parallel structure in performing conditional independence tests, and the computation can be significantly accelerated by running in parallel on a multi-core computer or a parallel architecture. This book is intended to serve researchers and scientists interested in high-dimensional statistics, and graduate students in broad data science disciplines. 
Key Features: 
A general framework for learning sparse graphical models with conditional independence tests 
Complete treatments for different types of data, Gaussian, Poisson, multinomial, and mixed data 
Unified treatments for data integration, network comparison, and covariate adjustment 
Unified treatments for missing data and heterogeneous data 
Efficient methods for joint estimation of multiple graphical models 
Effective methods of high-dimensional variable selection Effective methods of high-dimensional inference

Sparse Graphical Modeling for High Dimensional Data (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)

Computers & Technology

Liang, Faming

Jia, Bochao

Software

Chapman and Hall/CRC

Markov Chain Monte Carlo (MCMC) methods are now an indispensable tool in scientific computing. This book discusses recent developments of MCMC methods with an emphasis on those making use of past sample information during simulations. The application examples are drawn from diverse fields such as bioinformatics, machine learning, social science, combinatorial optimization, and computational physics. 
Key Features: <ul>
<li>Expanded coverage of the stochastic approximation Monte Carlo and dynamic weighting algorithms that are essentially immune to local trap problems.</li> <li>A detailed discussion of the Monte Carlo Metropolis-Hastings algorithm that can be used for sampling from distributions with intractable normalizing constants.</li> <li>Up-to-date accounts of recent developments of the Gibbs sampler.</li> <li>Comprehensive overviews of the population-based MCMC algorithms and the MCMC algorithms with adaptive proposals.</li> </ul>
This book can be used as a textbook or a reference book for a one-semester graduate course in statistics, computational biology, engineering, and computer sciences. Applied or theoretical researchers will also find this book beneficial.

Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples

Science & Math

Liu, Chuanhai

Carroll, Raymond

Biological Sciences

wiley

Markov Chain Monte Carlo (MCMC) originated in statistical physics, but has spilled over into various application areas, leading to a corresponding variety of techniques and methods. That variety stimulates new ideas and developments from many different places, and there is much to be gained from cross-fertilization. This book presents five expository essays by leaders in the field, drawing from perspectives in physics, statistics and genetics, and showing how different aspects of MCMC come to the fore in different contexts. The essays derive from tutorial lectures at an interdisciplinary program at the Institute for Mathematical Sciences, Singapore, which exploited the exciting ways in which MCMC spreads across different disciplines.

MARKOV CHAIN MONTE CARLO: INNOVATIONS AND APPLICATIONS (Lecture Notes Series, Institute for Mathematical Sciences, National University of Singapore)

Kendall, Dr Wilfrid S

Wang, Jian-Sheng

Mathematics

World Scientific Publishing Company

See the best price to sell, buy, or rent books by Liang, Faming. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Liang, Faming