[Topic-models] Books for getting up to speed

David Blei blei at CS.Princeton.EDU
Thu Sep 28 18:32:14 EDT 2006


i also like gelman et al., "bayesian data analysis."  variational  
inference isn't covered, but the treatment of probabilistic modeling  
is excellent.

best,
dave


On Sep 28, 2006, at 10:59 AM, Hal Daume III wrote:

> Chris Bishop's new book is quite good, and contains more or less
> everything you need in order to understand all the sampling and
> variational inference stuff.
>
>    http://research.microsoft.com/~cmbishop/PRML/index.htm
>
> Joel Reymont wrote:
>> Folks,
>>
>> All the theory behind topic modeling is foreign science to me given
>> my single year of college. Are there particular books that you would
>> recommend that would bring me up to par?
>>
>> I imagine I'll need a few college courses worth of books but learning
>> is my greatest strength and I do have some money to spare. My biggest
>> trouble is translating the math notation into code.
>>
>> I already bought a few books from Amazon, starting with the basics:
>>
>> Probability demistified: A self-teaching guide.
>> http://tinyurl.com/pk4xu
>>
>> Linear Algebra: A Modern Introduction
>> David Poole
>> http://tinyurl.com/owg7w
>>
>> Learning Bayesian Networks
>> Richard E. Neapolitan
>> http://tinyurl.com/qmmho
>>
>> Foundations of Statistical Natural Language Processing
>> Christopher D. Manning, Hinrich Schutze
>> http://tinyurl.com/psarl
>>
>> Text Mining: Predictive Methods for Analyzing Unstructured  
>> Information
>> Sholom M. Weiss, Nitin Indurkhya, Tong Zhang, Frederick Damerau
>> http://tinyurl.com/ohtqf
>>
>> 	Thanks, Joel
>>
>> --
>> http://wagerlabs.com/
>>
>>
>>
>>
>>
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>
> -- 
>   Hal Daume III --- me AT hal3 DOT name  |  http://hal3.name
>   "Arrest this man, he talks in maths."  |  http://nlpers.blogspot.com
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