[R] books about MCMC to use MCMC R packages?
Dear list users,
I need to learn about MCMC methods, and since there are several packages in
R that deal with this subject, I want to use them.
I want to buy a book (or more than one, if necessary) that satisfies the
following requirements:
- it teaches well MCMC methods;
- it is easy to implement numerically the ideas of the book, and notation
and concepts are similar to the corresponding R packages that deal with MCMC
methods.
I have done a search and 2 books seem to satisfy my requirements:
- Markov Chain Monte Carlo In Practice, by W.R. Gilks and others.
- Monte Carlo Statistical methods, Robert and Casella.
What do people think about these books? Is there a suggestion of some other
book that could satisfy better my requirements?
Thank you very much in advance.
The information contained herein is confidential and is inte...{{dropped}}
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Re: [R] books about MCMC to use MCMC R packages?
Hello,
I don't know yet of any book which presents MCMC methods with R examples
so I can't answer to this part of your question. But I can suggest some
general references (see the attached BibTeX file for details):
My favorite starting point is Radford Neal review from 1993, you can
download it from his web-site.
Julian Besag's 2000 working paper is also a good starting point
especially for statisticians (you can also download it).
If you're not scared at seeing the minus log likelihood referred to as
the energy you can take a look at the Physics literature (Sokal, 1996;
Berg 2004 and 2004b). It's a good way to learn about tricks physicists
use to get faster relaxation of their chains, like simulated annealing
and the replica exchange method / parallel tempering method. These
tricks were apparently first found by statisticians (Geyer, 1991; Geyer
Thompson, 1995; Ogata, 1995; review by Iba, 2001) but don't seem to
attract much attention in this community. In my experience they work
spectacularly well.
Robert and Casella, 2004 is a thorough reference with a bit too much on
reversible jump techniques and not enough on physicians tricks (in my
opinion of course).
Liu, 2001 is a spectacular overview. He knows very well both the
statistical and physical literatures. But it's often frustrating because
not enough details are given (for slow guys like me at least).
Fishman, 1996 is very comprehensive with much more than MCMC (that he
calls random tours).
Finally a note of caution about MCMC method can be useful, see Ripley, 1996.
I hope that helps,
Christophe.
Molins, Jordi wrote:
Dear list users,
I need to learn about MCMC methods, and since there are several packages in
R that deal with this subject, I want to use them.
I want to buy a book (or more than one, if necessary) that satisfies the
following requirements:
- it teaches well MCMC methods;
- it is easy to implement numerically the ideas of the book, and notation
and concepts are similar to the corresponding R packages that deal with MCMC
methods.
I have done a search and 2 books seem to satisfy my requirements:
- Markov Chain Monte Carlo In Practice, by W.R. Gilks and others.
- Monte Carlo Statistical methods, Robert and Casella.
What do people think about these books? Is there a suggestion of some other
book that could satisfy better my requirements?
Thank you very much in advance.
The information contained herein is confidential and is inte...{{dropped}}
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
--
A Master Carpenter has many tools and is expert with most of them.If you
only know how to use a hammer, every problem starts to look like a nail.
Stay away from that trap.
Richard B Johnson.
--
Christophe Pouzat
Laboratoire de Physiologie Cerebrale
CNRS UMR 8118
UFR biomedicale de l'Universite Paris V
45, rue des Saints Peres
75006 PARIS
France
tel: +33 (0)1 42 86 38 28
fax: +33 (0)1 42 86 38 30
web: www.biomedicale.univ-paris5.fr/physcerv/C_Pouzat.html
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] books about MCMC to use MCMC R packages?
This is the same mail as the previous one with a visible bibliography
this time (sorry)...
Hello,
I don't know yet of any book which presents MCMC methods with R examples
so I can't answer to this part of your question. But I can suggest some
general references (see the attached BibTeX file for details):
My favorite starting point is Radford Neal review from 1993, you can
download it from his web-site.
Julian Besag's 2000 working paper is also a good starting point
especially for statisticians (you can also download it).
If you're not scared at seeing the minus log likelihood referred to as
the energy you can take a look at the Physics literature (Sokal, 1996;
Berg 2004 and 2004b). It's a good way to learn about tricks physicists
use to get faster relaxation of their chains, like simulated annealing
and the replica exchange method / parallel tempering method. These
tricks were apparently first found by statisticians (Geyer, 1991; Geyer
Thompson, 1995; Ogata, 1995; review by Iba, 2001) but don't seem to
attract much attention in this community. In my experience they work
spectacularly well.
Robert and Casella, 2004 is a thorough reference with a bit too much on
reversible jump techniques and not enough on physicians tricks (in my
opinion of course).
Liu, 2001 is a spectacular overview. He knows very well both the
statistical and physical literatures. But it's often frustrating because
not enough details are given (for slow guys like me at least).
Fishman, 1996 is very comprehensive with much more than MCMC (that he
calls random tours).
Finally a note of caution about MCMC method can be useful, see Ripley,
1996.
I hope that helps,
Christophe.
PS: the bibliography
@TechReport{Neal_1993,
Author = {Neal, Radford M},
Title = {Probabilistic {I}nference {U}sing {M}arkov {C}hain
{M}onte {C}arlo {M}ethods},
Institution= {Department of Computer Science. University of Toronto},
Number = {CRG-TR-91-1},
web= {http://www.cs.toronto.edu/~radford/papers-online.html},
year = 1993
}
@TechReport{Besag_2000,
Author = {Besag, Julian},
Title = {Markov {C}hain {M}onte {C}arlo for {S}tatistical
{I}nference},
Type = {Working Paper},
Number = {9},
Abstract = {These notes provide an introduction to Markov chain
Monte Carlo methods that are useful in both Bayesian
and frequentist statistical inference. Such methods
have revolutionized what can be achieved
computationally, primarily but not only in the Bayesian
paradigm. The account begins by describing ordinary
Monte Carlo methods, which, in principle, have exactly
the same goals as the Markov chain versions but can
rarely be implemented. Subsequent sections describe
basic Markov chain Monte Carlo, founded on the Hastings
algorithm and including both the Metropolis method and
the Gibbs sampler as special cases, and go on to
discuss more recent developments. These include Markov
chain Monte Carlo p-values, the Langevin-Hastings
algorithm, auxiliary variables techniques, perfect
Markov chain Monte Carlo via coupling from the past,
and reversible jumps methods for target spaces of
varying dimensions. Specimen applications, drawn from
several different disciplines, are described throughout
the notes. Several of these appear for the first time.
All computations use APL as the programming language,
though this is not necessarily a recommendation! The
author welcomes comments and criticisms.},
eprint = {http://www.csss.washington.edu/Papers/wp9.pdf},
URL= {http://www.csss.washington.edu/Papers/},
month = sep,
year = 2000
}
@Book{Liu_2001,
Author = {Liu, Jun S.},
Title = {Monte {C}arlo {S}trategies in {S}cientific {C}omputing},
Publisher = {Springer Verlag},
Series = {Springer Series in Statistics},
Edition= {First},
year = 2001
}
@Book{RobertCasella_2004,
Author = {Robert, Christian P. and Casella, George},
Title = {Monte {C}arlo statistical methods},
Publisher = {Springer-Verlag},
Series = {Springer Texts in Statistics},
Address= {New York},
Edition= {Second},
isbn = {0-387-21239-6},
year = 2004
}
@InCollection{Sokal_1996,
Author = {Sokal, A.},
Title = {Monte {C}arlo methods in statistical mechanics:
foundations and new algorithms},
BookTitle = {Functional
Re: [R] books about MCMC to use MCMC R packages?
Hi Jordi, As far as implementions are concerned the book of Bernd Berg seems to be the closest to what you're looking for. You can find a link to the Fortran codes implementing the methods he describes from his web site: http://www.csit.fsu.edu/~berg/ There is also a nice reference for the analysis of the output of MCMC algorithm by Wolfhard Janke: Janke W (2002) Statistical Analysis of Simulations: Data Correlations and Error Estimation. In, Quantum Simulations of Complex Many-Body Systems: From Theory to Algorithms, Lecture Notes, J. Grotendorst, D. Marx, A. Muramatsu (Eds.), John von Neumann Institute for Computing, Jülich, NIC Series, Vol. *10*, pp. 423-445. http://www.fz-juelich.de/nic-series/volume10 Christophe. Molins, Jordi wrote: Hi Christophe, thank you very much for your detailed answer! I am not scared about physics literature, because I am a physicist myself, working in finance. So your suggestions suit me very well. What I would like is to implement numerically these methods. Is there some that goes closer into implementation? Thanks! Jordi -- A Master Carpenter has many tools and is expert with most of them.If you only know how to use a hammer, every problem starts to look like a nail. Stay away from that trap. Richard B Johnson. -- Christophe Pouzat Laboratoire de Physiologie Cerebrale CNRS UMR 8118 UFR biomedicale de l'Universite Paris V 45, rue des Saints Peres 75006 PARIS France tel: +33 (0)1 42 86 38 28 fax: +33 (0)1 42 86 38 30 web: www.biomedicale.univ-paris5.fr/physcerv/C_Pouzat.html __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] books about MCMC to use MCMC R packages?
I've found Bayesian Data Analysis by Gelman, Carlin, Stern Rubin
(2nd ed) to be quite useful for understanding how MCMC can be used for
Bayesian models. It has a little bit of R code in it too.
-- Tony Plate
Molins, Jordi wrote:
Dear list users,
I need to learn about MCMC methods, and since there are several packages in
R that deal with this subject, I want to use them.
I want to buy a book (or more than one, if necessary) that satisfies the
following requirements:
- it teaches well MCMC methods;
- it is easy to implement numerically the ideas of the book, and notation
and concepts are similar to the corresponding R packages that deal with MCMC
methods.
I have done a search and 2 books seem to satisfy my requirements:
- Markov Chain Monte Carlo In Practice, by W.R. Gilks and others.
- Monte Carlo Statistical methods, Robert and Casella.
What do people think about these books? Is there a suggestion of some other
book that could satisfy better my requirements?
Thank you very much in advance.
The information contained herein is confidential and is inte...{{dropped}}
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
