nor
).The sort of reasoning we wish to perform on the model is finding out the probability distribution of some of its random variables. For example, we can work out from the model that the probability of the grass being wet is 60.6%. Such reasoning is called probabilistic inference. Often we are interested in the distribution conditioned on the fact that some random variables have been observed to hold a particular value. In our example, having observed that the grass is wet, we want to find out the chance it was raining on that day. For background on the statistical modeling and inference, the reader is referred to Pearl's classic text and to Getoor and Taskar's collection.
Lise Getoor and Ben Taskar: Introduction to Statistical Relational Learning
MIT Press, 2007.
David Wingate, Andreas Stuhlmueller and Noah D. Goodman: Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation.
AISTATS2011. Revision 3. February 8, 2014.
PPS2017-poster.pdf [98K]
Poster at PPS 2017
oleg-at-okmij.org
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