[SciPy-user] is there any plan to import BNT(bayesian network toolkit) from matlab?

[Karl Young]

>
>>. What is the title of Chris Bishop's book ?
>>    
>>
>
>I believe he's referring to "Pattern Recognition and Machine  
>Learning", which was published in 2006. It's quickly become a  
>favourite in many circles, and it contains one of the most  
>comprehensive treatments of graphical models that I know of in any  
>textbook.  The website for the book: http://research.microsoft.com/ 
>~cmbishop/PRML/
>
>Conveniently enough for all interested, the graphical models chapter  
>is available as a free sample chapter!
>
>http://research.microsoft.com/~cmbishop/PRML/Bishop-PRML-sample.pdf
>
>Stefan, I think that Bishop's chapters on graphical models, Markov  
>Chain Monte Carlo, and variational methods are an excellent place to  
>start.
>
>Off the top of my head, these papers are rather illuminating as well:
>
>http://www.cs.toronto.edu/~roweis/papers/NC110201.pdf
>http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=910572
>http://www.cs.toronto.edu/~radford/em.abstract.html
>
>Once I'm done my coursework for the term and have more time on my  
>hands I'll be contributing whatever I can to the effort
>  
>
Thanks for the references ! For me the "mahine learning bibles" have 
been the Weka "Data Mining" book and Hastie, Tibshirani, and Friedman's 
book  ("The Elements of Statistical Learning")  but save for a few lines 
in the Weka book there isn't anything in those on Bayes nets. For Bayes 
nets I've been trying to get through Spirtes, et. al.'s  "Causation, 
Prediction and Search" but the chapter from Murphy's book on graphical 
models looks great; I look forward to reading it and the other 
references above. 

-- KY

Karl Young
Center for Imaging of Neurodegenerative Diseases, UCSF          
VA Medical Center (114M)              Phone:  (415) 221-4810 x3114  lab        
4150 Clement Street                   FAX:    (415) 668-2864
San Francisco, CA 94121               Email:  karl young at ucsf edu