Advanced Markov Chain Monte Carlo Methods

Regular price €107.99
A01=Chuanhai Liu
A01=Faming Liang
A01=Raymond Carroll
Age Group_Uncategorized
Age Group_Uncategorized
application
Author_Chuanhai Liu
Author_Faming Liang
Author_Raymond Carroll
automatic-update
bioinformatics
book
carlo
Category1=Non-Fiction
Category=PBKS
chain
COP=United States
Delivery_Delivery within 10-20 working days
diverse
drawn
dynamic
emphasis
eq_isMigrated=2
features
fields
indispensable
information
Language_English
local
markov
mcmc
methods
monte
PA=Available
past
Price_€100 and above
PS=Active
recent developments
sample
scientific
softlaunch
stochastic
tool
use

Product details

  • ISBN 9780470748268
  • Weight: 737g
  • Dimensions: 163 x 233mm
  • Publication Date: 16 Jul 2010
  • Publisher: John Wiley & Sons Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
Delivery/Collection within 10-20 working days

Our Delivery Time Frames Explained
2-4 Working Days: Available in-stock

10-20 Working Days
: On Backorder

Will Deliver When Available
: On Pre-Order or Reprinting

We ship your order once all items have arrived at our warehouse and are processed. Need those 2-4 day shipping items sooner? Just place a separate order for them!

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:

  • Expanded coverage of the stochastic approximation Monte Carlo and dynamic weighting algorithms that are essentially immune to local trap problems.
  • A detailed discussion of the Monte Carlo Metropolis-Hastings algorithm that can be used for sampling from distributions with intractable normalizing constants.
  • Up-to-date accounts of recent developments of the Gibbs sampler.
  • Comprehensive overviews of the population-based MCMC algorithms and the MCMC algorithms with adaptive proposals.

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.

Faming Liang, Associate Professor, Department of Statistics, Texas A&M University.

Chuanhai Liu, Professor, Department of Statistics, Purdue University.

Raymond J. Carroll, Distinguished Professor, Department of Statistics, Texas A&M University.