5 comments about Monte Carlo Methods in Bayesian Computation (Springer Series in Statistics).

1. 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.



2. 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.



3. 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.



4. 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.



5. With advances in computing and the rediscovery of Markov Chain Monte Carlo methods and their application to Bayesian methods, there have been a number of books written on this subject in recent years. What then distinguishes this text from the others?
   
   Section 1.1 of the text "Aims" provides the objectives of the book and compares it to the other recent major works. Basically, the authors say that Tanner (1996), Gilks, Richardson and Spiegelhalter (1996), Gamerman (1997), Robert and Casella (1999) and Gelfand and Smith (2000) all offer developments in MCMC sampling. So this text only provides a brief but hopefully sufficient introduction to MCMC sampling.
   
   The main objective of the book is to develop more advanced Monte Carlo methods that speed up the computational time for specialized Bayesian problems. Problems of interest to the authors include estimating posterior means, modes and standard deviations, Bayesian equivalent of p-values, marginal posterior densities, marginal likelihoods, Bayes factors, posterior model probabilities, Bayesian credible intervals (the Bayes analogue to frequentist confidence intervals) and highest posterior probability density intervals.
   
   Chapter 1 sets the stage. It provides the objectives, an outline of the rest of the book and a list of motivating examples that will be used throughout the text.
   
   Chapter 2 then provides the brief introduction to MCMC sampling. Some theory is provided, many useful references are cited and several ideas are well illustrated through examples and figures.
   
   Chapter 3 is also introductory in nature showing how the methods of Chapter 2 can be applied to obtain various estimates based on the approximated posterior probability distribution.
   
   The rest of the book deals with specialized topics and techniques important to Bayesian inference. The book contains a wealth of theory and a good mix of applications and challenging research problems. The authors are experienced contributors to this literature.
   
   It is intended as an advanced graduate course for Ph.D. statistics student in their second or third year of graduate study. It also will serve statistical researchers with an excellent reference both for the practice and development of Bayesian inference. Applications in the area of biostatistics are emphasized but the methods apply to Bayesian statistical inference in all fields.

No comments about Further Mathematics for the Physical Sciences.

No comments about Essential Quantum Mechanics.

No comments about Stochastic Tools in Turbulence.

1 comments about Problems and Solutions in Quantum Mechanics.

1. This is a superb difficult book, not for the faint-hearted. It contains solutions involving Green's Functions and Propagators, not usully found in such books. Don't look for detailed solutions to the "usual suspects" - if they're there, they are there to support the more challening problems. The solutions do not always include as many steps as I like to include. Filling them in adds to one's understanding of the method of solution. I didn't always like the path the book tread and followed a different one - at least I had something to compare to 5 pages later. (The book's solution was nearly always (if not, always) superior to mine. But you know "My solution, right or wrong, but my solution!"
   
   The problems are at a level (and some correspond to) the text by Mertzbacher. I showed the book to my QM professor and his response was to write down the ISBN. Not every type of problem at this level is solved and I would get Zetelli's "Quantum Physics" to fill in gaps, say in WKB and Angular Momentum. There are a number of other useful books, for example, "Problems and Solutions in Quantum Mechanics" written in China based on US university qualifying exam problems, and a short, but very good, book from Cambridge U. Press: Squires - "Problems in Quantum Mechanics - with Solutions" which covers problems through time-dependent perturbation theory.

1 comments about Scaling and Renormalization in Statistical Physics (Cambridge Lecture Notes in Physics).

1. This book deals with a most interesting and dynamical area of modern research in physics.
   In the frontmatters, Cardy explains that, too often, in the past, students who were willing to go into research in condensed matter as quickly as possible, found a difficult time struggling and mastering techniques that were initially devised for particule physics.
   K. Wilson built an arch between these two sub-fields of physics, through his Nobel-prize-worth insights.
   However, as Cardy rightly points out, renormalization group methods can be more efficiently explained, learnt and applied by condensed matter, in a Feynman-diagram inspired presentation of the perturbative renormalization group. This subject then reduce to relatively simple combinatorics.
   This is one of this book main contribution.
   After explaining carefully scaling concepts and the various corresponding laws of critical phenomena, as well as field theory and Landau-Ginzburg theory, Cardy goes on to explain those from renormalization group methods.
   The usual (exactly solved) statistical models appear at various parts of the book.
   Welcome additions to the literature are chapters on random systems (those with impureties), polymer physics and critical dynamics.
   A final chapter offers a primer on how conformal symmetry pops out at the critical point. This is a most active subject of current research in the field.
   I should stress however that this book is meant for advanced students.
   The typical student would have a background in phase transitions and statistical physics, as well as prior acquaintance with basic (quantum)field theory.
   This book would probably be more profitably read with a more experienced student or a teacher at hand, for it is written in a rather condensed form and due to the vastness of the subject can hardly give an overview of it.

No comments about Multivalued Fields: In Condensed Matter, Electromagnetism, and Gravitation.

1 comments about Computational Physics: An Introduction.

1. ....provides a good working knowledge of the techniques used to solve problems in hydrodynamics, quantum mechanics, and statistical mechanics. To get the most out of the book, the reader should be familiar with vector notation (div, grad and curl), matrix algebra, the theory of ordinary and partial differential equations, and Monte-Carlo simultation techniques. The 2nd edition of Franz Vesely's book includes recent developments in lattice-gas cellular models for hydrodynamics such as the Lattice Boltzmann method. This is an advance on the Frisch, Hasslacher and Pomeau model. The conclusion is that any book attempting to contemporise the field of computational physics will always be worth the expenditure; it not only reaffirms background theory, but also points the way to new ideas and fresh applications. Review by Paul Barker

2 comments about Theoretical Concepts in Physics: An Alternative View of Theoretical Reasoning in Physics.

1. I recommend this book for final undergraduate and beginning graduate students in the Physical Sciences. As a PhD student in Astrophysics, this book has been very valuable and enjoyable for me. The historical perspective is also a very nice aspect of this book.



2. Although it does not teach you methods in Theoretical Physics(such as Morse and Feshbach or Theoretical Physics by George Joos), it does tell you how the great physicists got to bring about the great physical "Theorems". But to be fair, he tells you enough about each great theory there is out there by taking it up as a case study based on some fundamental(amthematical and physical) knowledge. I think it's a brilliant book from both a historical perspective as well as from a informative standpoint. I've had it for years and love the book

2 comments about Lectures on Partial Differential Equations (Universitext).

1. OK, I did not read the book, just browsed through the pages. My first impression is that this is an excellent book, like most of Arnold's books. It is not trying to cover the theory of Partial Differential Equations, and this cannot be done in a single book anyway - just selected topics on a very accessible level. The geometric insight is excellent and even a specialist can benefit from that. I highly recommend this book to undergraduates (and higher) that are passionate about analysis.
   
   I will update my review after I actually buy the book, I understand that I am not very helpful at the moment.
   
   Update: I read parts of the book and I still like it a lot. I however found to be aimed at more advanced readers. It is an excellent book to complement any of the standard PDE texts.



2. Arnold's geometric point of view of differential equations is really intriguing. These lectures treat the subject of PDEs by considering specific examples and studying them thoroughly. By this approach the author aims at presenting fundamental ideas in a clear way. However, the interetested reader should be acquainted with some geometric ideas of differential equations.