It&#x27;s by Stuart Russell&#x27;s group at Berkeley (he coauthored the AI bible with Peter Norvig). Their tutorial: <a href="http:&#x2F;&#x2F;bayesianlogic.github.io&#x2F;download&#x2F;BLOG-tutorial-2014.pdf" rel="nofollow">http:&#x2F;&#x2F;bayesianlogic.github.io&#x2F;download&#x2F;BLOG-tutorial-2014.p...</a> Page 58 has some good sample code. Semantics are on page 70.<p><i>Every well-formed BLOG model specifies a unique proper probability distribution over all possible worlds definable given its vocabulary
•No infinite receding ancestor chains;
•no conditioned cycles;
•all expressions finitely evaluable;
•Functions of countable sets</i><p>They instantiate some parts of the network and do inference with MCMC. I wonder how it compares to the Markov Logic approach from the University of Washington.