MCMC is now passing all tests. It is still not totally clear if it is perfect or not yet though. Though it passes all of the tests most of the time, occasionally it misses some of them. I have been comparing it to Kevin Murphy's matlab toolbox. Since it is an approximate inference algorithm, I think the problem is that it is just using a different random number generator than matlab, and hence is giving slightly different answers. I will come back to it and test it more exhaustively when I have built more complex models.

I have a version of update parameters working. I have been working on EM. I am most of the way toward EM, though I ran into a small snag using non MCMC algorithms as the underlying inference, and I wanted to wait until I had a chance to discuss this problem with a real statistician. In the mean time I am moving on to dynamic bayes nets.

My goal with DBNs is to start by implementing a raw enumerative engine first, which just does a raw unrolling of the DBN during inference. I will subsequently move on to a more efficient approach.

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