> Russell Almond wrote:
>
>> I'm looking for a good survey article on Learning in Bayesian Networks.
>> Does anybody know of something like that?
>>
>> I will summarize replies to the list.
>>
Here is my promised summary.
The articles I was remembering but not remembering the reference for were:
> @techreport{ david95tutorial,
> author = "Heckerman, David",
> title = "{A} {T}utorial on {L}earning {B}ayesian {N}etworks",
> number = "MSR-TR-95-06",
> month = "March",
> year = "1995",
> url = "citeseer.nj.nec.com/article/heckerman95tutorial.html" }
>
@article{ buntine96guide,
author = "W. Buntine",
title = "A guide to the literature on learning probabilistic networks from data",
journal = "Ieee Trans. On Knowledge And Data Engineering",
volume = "8",
address = "Thinkbank, 1678 Shattuck Ave, Suite 320, Berkeley, Ca, 94709",
pages = "195--210",
year = "1996",
url = "citeseer.nj.nec.com/buntine96guide.html" }
I was also referred to:
>
> @book{Jor98,
> author = "M. Jordan",
> title = "Learning in Graphical Models",
> publisher = "MIT Press",
> address = "Cambridge",
> year = "1998" }
>
> Hope this helps,
This book contains the Heckerman tutorial, along with several other
articles describing more recent research.
I also discovered that
\refrence{Cowell, R.G., A.P. Dawid, S.L. Lauritzen, and
D.J. Spiegelhalter [1999]} {\it Probabilistic Networks and Expert
Systems\/}. Springer.
Has several chapters on learning issues towards the end of the book.
Other suggestions I received:
> @techreport{ buntine94learning,
> author = "W. L. Buntine",
> title = "Learning With Graphical Models",
> number = "FIA-94-02",
> address = "NASA Ames Research Center",
> year = "1994",
> url = "citeseer.nj.nec.com/article/buntine94learning.html" }
>
This may actually be:
\refrence {Buntine, W.L. [1994]} ``Operations for Learning with
Graphical Models.'' {\it Journal of Artificial Intelligence
Research.\/} {\bf 2\/}, 159--225.
> Zoubin's paper is specifically on learning dynamic
> probabilistic models, and is well written and of
> tutorial quality:
>
> @Article{Gha98,
> author = "Z. Ghahramani",
> title = "Learning Dynamic {Bayesian} Networks",
> journal = "Lecture Notes in Computer Science",
> volume = "1387",
> pages = "168--198",
> year = "1998",
> coden = "LNCSD9",
> ISSN = "0302-9743",
> bibdate = "Sat Oct 10 14:40:24 MDT 1998",
> acknowledgement = ack-nhfb,
> }
>
> I have a very short survey paper
> Rish, I.(2000). Advances in Bayesian Learning. Proceedings of the 2000
> International Conference on Artificial Intelligence (IC-AI'2000). Las
> Vegas,
> you can find it on my web-page http://www.research.ibm.com/people/r/rish/
> .
> Another good paper by Heckerman et al.:
>
> @Article{HGC95,
> author = "David Heckerman and Dan Geiger and David M.
> Chickering",
> title = "Learning {B}ayesian networks: the combination of
> knowledge and statistical data",
> journal = "Machine Learning",
> volume = "20",
> number = "3",
> pages = "197--243",
> year = "1995",
> publisher = "Kluwer Academic Publishers, Boston",
> }
>
>
> You might also look for a copy of "Inference in Belief Networks: A
> Procedural
> Guide" by Cecil Huang and Adnan Darwiche from the International Journal of
> Approximate Reasoning 1994 11:1-158
>
>
> Paul Krause "Learning probabilistic networks" would be a good place to
> start (if a little dated now).
> it is NOT an article, and actually ment for non-computer scientists (or
> statisticians), but you might
> want to look at B-Course html-tutorial part for the data analysis service
>
> http://b-course.cs.helsinki.fi
>
> Although the data analysis service is state-of-the-art inside, we have
> made an attempt to provide a very gentle introduction to the somewhat
> hairy issue of real-life construction of Bayesian networks from data. I
> understand that if you are looking for a good general scientific survey,
> this "interactive introduction" is not what you need, but you might find
> it useful anyway. B-Course is currently used by thousands of researchers
> around the world and there will be a new release in two weeks (which
> includes classification).
Finally, I've attached the draft bibliography from the learning
section of a tutorial on Graphical Models in Educational Assessment
I'm currently working on.
- --oVlSxhCMT8
Content-Type: text/x-tex
Content-Description: Draft bibliography for learning part of NCME tutorial
Content-Disposition: inline;
filename="learningBib.tex"
Content-Transfer-Encoding: 7bit
%% -*- mode: plain-tex; -*-
%%**start of header
\def\refrence#1{\par\hang\hskip-\parindent {\bf #1\/}.}
%%**end of header
\refrence {Andersen, S.A., Madiagn, D. and Perlman, M.D. [1996a]} ``A
characterization of Markov equvilance classes for acyclic digraphs.''
{\it Annals of Statistics,\/}, {\bf 25\/}, 505-41.
\refernce {Andersen, S.A., Madigan, D. and Perlman, M.D. [1996b]} ``An
Alternative Markov Property for Chain Graphs.'' {\it Uncertainty in
Artificial Intelligence, Proceedings of the Twelfth Conference.\/}
Morgan Kaufmann.
\refrence {Buntine, W.L. [1994]} ``Operations for Learning with
Graphical Models.'' {\it Journal of Artificial Intelligence
Research,\/} {\bf 2\/}, 159--225.
\refrence {Buntine, W.L. [1996]} ``A guide to the literature on
learning probabilistic networks from data.'' {\it IEEE Trans. on
Knowledge and Data Engineering,\/} {\bf 8\/}, 195--210.
\refrence {Chickering, D. [1996]} ``Learning equivalence classes of
Bayesian-network structures.'' {\it Proceedings of the Eleventh
Conference on Uncertainty in Artificial Intelligence.\/} Morgan
Kaufmann. 87--98.
\refrence{Cooper, Gregory F. and Herskovits, Edward [1992]} ``A
Bayesian Method for the Induction of Probabilistic Networks from
Data.'' {\it Machine Learning\/} ({\bf 9\/}), 309--347.
\refrence{Cowell, R.G., A.P. Dawid, S.L. Lauritzen, and
D.J. Spiegelhalter [1999]} {\it Probabilistic Networks and Expert
Systems\/}. Springer.
\refrence {Dempster, A.P., Laird, N. and Rubin, D.B. [1972]} ``Maximum
likelihood from incomplete data via the EM algorithm.'' {\it JRSS
B\/}, {\bf 39\/}, 1--38.
\refrence {Draper, David [1995]} ``Assessment and Propagation of Model
Uncertainty.'' {\it JRSS B\/}, {\bf 57\/}, 45--98.
\refrence{Draper, D., Hodges, J.S., Leamer, E.E., Morris, C.N. and
Rubin, D.B. [1987]} ``A research agenda for assessment and
propagation of model uncertainty.'' {Rand Note N-2683-RC}, The RAND
Corporation, Santa Monica, California.
\refrence {Heckerman, D. [1995]} ``A tutorial on learning with
Bayesian networks.'' Technical Report MSR-TR-95-06, Microsoft
Research, March, 1995 (revised November, 1996). (Reprinted in Jordan
[1998]). {\tt ftp://ftp.research.microsoft.com/pub/tr/tr-95-06.pdf\/}
\refrence {Heckerman, D., Gieger, D., and Chikering, D. [1995a]}
``Learning Bayesian networks: The combination of knowledge and
statistical data.'' {\it Machine Learning,\/} {\bf 20,\/} 197--243.
\refrence {Jaakkola, T.S. and Jordan, M.I. [1998]} ``Improving the
Mean Field approximation via the use of Mixture Distrubutions.''
In Jordan (ed) [1998] {\it Learning in Graphical Models.\/} Kulwer
Academic Publishers. 163--173.
\refrence {Jordan, M.I. (ed) [1998]} {\it Learning in Graphical
Models.\/} Kulwer Academic Publishers. Reprinted by MIT Press.
\refrence { Jordan, M.I., Ghahramani, Z., Jaakkola, T.S. and Saul,
L.K. [1998]} ``An introduction to Variational Methods for Graphical
Models.'' In Jordan (ed) [1998] {\it Learning in Graphical
Models.\/} Kulwer Academic Publishers. 105--161.
\refrence {Madigan, D. and A.E. Raftery [1994]} ``Model Selection
and Accounting for Model Uncertainty in Graphical Models Using Occam's
Window.'' {\it JASA\/}, {\bf 89\/}, 1535--1546.
\refrence {Madigan, D., Raftery, A.E., Volinsky, C. and Hoeting,
J. [1996]} ``Bayesian Model Averaging.'' {\it Proceedings of the AAI
Workshop on Integrating Multiple Learned Models.\/}
\refrence {Madigan, D., J. Gavrin and A.E. Raftery
[1995]} ``Enhancing the Predictive Performance of Bayesian Graphical
Models.'' {\it Communications in Statistics: Theory and Methods,\/}
{\bf 24\/}, 2271--2292.
\refrence{Meil\u a, M. and Jordan, M.I. [2000]} ``Learning with
Mixtures of Trees.'' {\it Journal of Machine Learning Research\/},
{\bf 1\/}, 1--48.
\refrence {Rissanen, J. [1987]} ``Stochastic complexity (with
discussion).'' {\it JRSS B,\/} {\bf 49\/}, 223--265.
\refrence {Rubin, D.B. [1978]} ``Multiple imputation in sample surveys:
A pheonomenological Bayesian approach to nonresponse.'' {\it
Proceedings of the Survey Research Methods Section\/}, American
Statistical Association, pp 20-44.
\refrence {Schwarz, G. [1978]} ``Estimating the Dimension of a
Model.'' {\it Annals of Statistics\/}, {\bf 6\/}, 461--464.
\refrence {Shafer, G. [1996]} {\it The Art of Causal Conjecture.\/}
MIT Press.
\refrence {Spiegelhalter, D.J. and S.L. Lauritzen [1990]}
``Sequential Updating of Conditional Probabilities on Directed
Graphical Structures.'' {\it Networks,\/} {\bf 20\/}, 579--605.
\refrence {Spirtes, P., C. Meek and T.S. Richardson [1987]} ``A
polynomial-time alogrithm for deteriming DAG equivalence in the
presence of latent variables and selection bias.'' In Madigan and
Smythe (eds) {\it Preliminary papers of the Sixth International
Workshop on AI and Statistics\/}, 489--501.
\refrence {Wermuth, N. and S.L. Lauritzen [1990]} `` On
Substantive Research Hypotheses, Conditional Independence Graphs and
Graphical Chain Models.'' {\it Journal of the Royal Statistical
Society, Series B,\/} {\bf 52} 21--50.
- --oVlSxhCMT8
Content-Type: text/plain; charset=us-ascii
Content-Description: message body text
Content-Transfer-Encoding: 7bit
My thanks to Daniel Nikovski, Irina Rish, Ben Perry, Andy Novobilski,
Simon Parsons, Henry Tirri and Kristian Kersting for their
suggestions.
--Russell Almond
This archive was generated by hypermail 2b29 : Tue Mar 19 2002 - 15:40:01 PST