Book Description
This book examines advanced Bayesian computational methods. It presents methods for sampling from posterior distributions and discusses how to compute posterior quantities of interest using Markov chain Monte Carlo (MCMC) samples. This book examines each of these issues in detail and heavily focuses on computing various posterior quantities of interest from a given MCMC sample. Several topics are addressed, including techniques for MCMC sampling, Monte Carlo methods for estimation of posterior quantities, improving simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, highest posterior density interval calculations, computation of posterior modes, and posterior computations for proportional hazards models and Dirichlet process models. The authors also discuss computions involving model comparisons, including both nested and non-nested models, marginal likelihood methods, ratios of normalizing constants, Bayes factors, the Savage-Dickey density ratio, Stochastic Search Variable Selection, Bayesian Model Averaging, the reverse jump algorithm, and model adequacy using predictive and latent residual approaches. The book presents an equal mixture of theory and applications involving real data. The book is intended as a graduate textbook or a reference book for a one semester course at the advanced masters or Ph.D. level. It would also serve as a useful reference book for applied or theoretical researchers as well as practitioners. Ming-Hui Chen is Associate Professor of Mathematical Sciences at Worcester Polytechnic Institute, Qu-Man Shao is Assistant Professor of Mathematics at the University of Oregon. Joseph G. Ibrahim is Associate Professor of Biostatistics at the Harvard School of Public Health and Dana-Farber Cancer Institute.
Customer Reviews:
not a good starting point.......2004-12-19
You need to be clear what you are looking for. If you have vaguely heard that MCMC (Monte Carlo Markov Chain) methods are a neat way to apply Bayesian ideas to practical problems, and you want to use them, then this is *not* the book for you. Go to the splendid Gilks et al, Markov Chain Monte Carlo in Practice. Also check out BUGS, which is free software, originally written by Gilks and co and improved by many others.
If you want a more general introduction to Bayesian methods, then Gelman et al, Bayesian Data Analysis is excellent.
If you are unclear about the controversies and want to know why the Bayesian approach is correct, and the others are flat wrong, then read Ed Jaynes book.
So what is this book for. Well, I think you have to be a specialist, interested in further development of the techniques, and in the maths. As a previous reviewer has commented (correctly), in that case you probably have easy access to the journal literature and need to think carefully what extra benefits this book gives you.
Same writer reviewed book 4 times!.......2004-12-19
I depend upon the Amazon reviews to help determine whether to purchase a book as most others do. When a reviewer posts four 5 star reviews of the book (out of 7 total) it biases the rating and makes one wonder whether if the reviewer has an agenda or is related to the authors. This may be a great book, but I have no confidence from the rating given here.
extensive book on MCMC.......2002-10-18
This is truly an oustanding book on MCMC methods for Bayesian
computation. The authors present a nice balance between technical
developments and applications. It covers several topics not covered by other MCMC books, such as HPD regions, model selection, and density estimation. This book is world class.
two great books.......2002-10-17
This is an outstanding book on MCMC methods. The book presents
novel and sophisticated methods for carrying out posterior
computations and summarizing posterior quantities of interest using novel MCMC techniques. The authors present a lot of their
groundbreaking work as well as summarizing the work of many others. The book presents a number of complex models used in real and interesting applications in the biomedical sciences. Two of the authors also have wirtten another outstanding book titled Bayesian Survival Analysis (Ibrahim et al., 2001), which presents modern methods for Bayesian survival analysis and provides a comprehensive and thorough treatment of the subject. The authors are to be congratulated on writing two very fine books. Both books get 5 stars from me.
two great books.......2002-10-15
This is a great book by the authors, covering a wide range of
topics in MCMC. The coverage of the material is deep and novel.
Two of the authors also have published another outstanding book
titled Bayesian Survival Analyis, by Ibrahim et al., which presents
cutting edge and novel methods in the analysis of survival data.
Both books get 5 stars from me. A splendid job by the authors
in writing two very fine books.
Average customer rating:
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A Monte Carlo Primer: A Practical Approach to Radiation Transport
Stephen A. Dupree , and
Stanley K. Fraley
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Monte Carlo Primer: Volume 2
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Monte Carlo
ASIN: 0306467488 |
Book Description
This book introduces the reader to the use of Monte Carlo methods for solving practical problems in radiation transport, and will also serve as a reference work for practitioners in the field. It assumes the reader has a general knowledge of calculus and radiation physics, and a knowledge of Fortran programming, but assumes no prior knowledge of stochastic methods or statistical physics. The subject is presented by a combination of theoretical development and practical calculations. Because Monte Carlo methods are closely linked to the use of computers, from the beginning the reader is taught to convert the theoretical constructs developed in the text into functional software for use on a personal computer. Example problems provide the reader with an in-depth understanding of the concepts presented and lead to the production of a unique learning tool, a probabilistic framework code that models in a simple manner the features of production of Monte Carlo transport codes. This framework code is developed in stages such that every function is understood, tested, and demonstrated - random sampling, generating random numbers, implementing geometric models, using variance reduction, tracking particles in a random walk, testing the thoroughness with which the problem phase space is sampled, scoring detectors, and obtaining estimates of uncertainty in results. Advanced topics covered include criticality, correlated sampling, adjoint transport, and neutron thermalization.
Monte Carlo codes can produce highly precise wrong answers. The probability of this occurring is increased if production codes are run as opaque, `black boxes' of software. This text attempts to make Monte Carlo into a comprehensible, usable tool for solving practical transport problems. It is suitable for advanced undergraduate and graduate students and researchers who wish to expand their knowledge of the Monte Carlo technique.
Book Description
This book provides an introduction to Monte Carlo simulations in classical statistical physics and is aimed both at students beginning work in the field and at more experienced researchers who wish to learn more about Monte Carlo methods. The material covered includes methods for both equilibrium and out of equilibrium systems, and common algorithms like the Metropolis and heat-bath algorithms are discussed in detail, as well as more sophisticated ones such as continuous time Monte Carlo, cluster algorithms, multigrid methods, entropic sampling and simulated tempering. Data analysis techniques are also explained starting with straightforward measurement and error-estimation techniques and progressing to topics such as the single and multiple histogram methods and finite size scaling. The last few chapters of the book are devoted to implementation issues, including discussions of such topics as lattice representations, efficient implementation of data structures, multispin coding, parallelization of Monte Carlo algorithms, and random number generation. At the end of the book the authors give a number of example programmes demonstrating the applications of these techniques to a variety of well-known models.
Customer Reviews:
A practical book on Monte Carlo in Stat. Phy........2006-07-11
Overall, it's an excellent book on the practice of Monte Carlo and the c++ code in appendix are very instructive (Random Number Generators, Solid Monte Carlo Routines, etc.). It does have certain weaknesses though.
1) Sometimes the description are trivial in principle but written in great details. For example, on Pg 58 on the exact methods (so-called 'efficient way') of calculating averaged quantities from simulation.
2) Most of the content are heuristic. The discussion of the whole book is based on practice, although you do find something looks like a rigious proof (but no in fact). By rigious, I mean the proof should be based on Markov Chain and related properties of random process and statistical physics.
But as I said in the beginning, this is a invaluable book to anyone who wants to use Monte Carlo method in his/her domain. For myself, I am using this book as a reference to tackle functional optimization - Simulated Annealing, which is a very close sibling of Monte Carlo method.
One of the best and up-to-date books in market.......2000-03-27
This book covers a wide range of applications in Statistical Mechanics, with clear explanation, examples, tips, algorithms, and explicit programs at the end of the book. It is good for beginners and experienced alike, since it discusses "classical" and modern algorithms. It is a must for those who want to make actual numerical calculations in Statistical Physics.
Book Description
This new and updated edition deals with all aspects of Monte Carlo simulation of complex physical systems encountered in condensed-matter physics, statistical mechanics, and related fields. After briefly recalling essential background in statistical mechanics and probability theory, it gives a succinct overview of simple sampling methods. The concepts behind the simulation algorithms are explained comprehensively, as are the techniques for efficient evaluation of system configurations generated by simulation. It contains many applications, examples, and exercises to help the reader and provides many new references to more specialized literature. This edition includes a brief overview of other methods of computer simulation and an outlook for the use of Monte Carlo simulations in disciplines beyond physics. This is an excellent guide for graduate students and researchers who use computer simulations in their research. It can be used as a textbook for graduate courses on computer simulations in physics and related disciplines.
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Advanced Monte Carlo for Radiation Physics, Particle Transport Simulation and Applications
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ASIN: 3540417958 |
Book Description
This book focusses on the state of the art of Monte Carlo methods in radiation physics and particle transport simulation and applications, the latter involving in particular, the use and development of electron--gamma, neutron--gamma and hadronic codes. Besides the basic theory and the methods employed, special attention is paid to algorithm development for modeling, and the analysis of experiments and measurements in a variety of fields ranging from particle to medical physics.
Product Description
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.
Book Description
Monte Carlo Simulation in Statistical Physics deals with the computer simulation of many-body systems in condensed-matter physics and related fields of physics, chemistry and beyond, to traffic flows, stock market fluctuations, etc.). Using random numbers generated by a computer, probability distributions are calculated, allowing the estimation of the thermodynamic properties of various systems. This book describes the theoretical background to several variants of these Monte Carlo methods and gives a systematic presentation from which newcomers can learn to perform such simulations and to analyze their results. This fourth edition has been updated and a new chapter on Monte Carlo simulation of quantum-mechanical problems has been added. To help students in their work a special web server has been installed to host programs and discussion groups (http://wwwcp.tphys.uni-heidelberg.de). Prof. Binder was the winner of the Berni J. Alder CECAM Award for Computational Physics 2001.
Customer Reviews:
A graduate level book (Review for the 4th edition).......2007-03-09
This is a nice little book written by two experts of the field. This edition is only an expanded version of earlier editions (by addition of two new chapters, the core of the book chapter 1 to 3 hasn't change at all). The book covers monte carlo techniques through various well-known examples (Ising model, random walk, percolation, self-avoiding random walk). I enjoyed reading the first 3 chapters of the book. In particular, chapter 3 guides the readers and gives them the chance to practice what they should have learned in previous chapter (through 53 exercises). The following 2 chapters (chapter 4 and 5) are not as nicely written. Moreover, there are some serious shortcoming in the book. (1) All codes are written in Fortran. While everyone who can program can easily understand the codes, Fortran belongs to the past and could have been ok for physics students during late 80's (first edition) but not for those at 2006. (2) The guide (chapter 3) should have been the last chapter and have covered subjects in chapters 4 and 5 (3) As I mentioned before, chapter 4 and 5 are not well-organized. (4) The book in general stresses too much on finite-size effects. However, it is an important subject and it tells us how we can scale our simulation result to more realistic cases. By my judgement, the book gives wrong impression about the degree of its importance.
I recommend graduate students who are serious about learning monte carlo methods to read Newman and Barkema book (Monte Carlo Methods in Statistical Physics) instead since it provides a broader view about the subject. Although I highly recommend those who are interested in the subject to go through chapter 3. It is fun and very instructive.
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Applications of the Monte Carlo Method in Statistical Physics (Topics in Current Physics)
K. Binder
Manufacturer: Springer
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Binding: Paperback
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ASIN: 0387176500 |
Books:
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