About Parameter Learning

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About Parameter Learning

Post by JohnLee » Sat Dec 01, 2018 1:02 am

When I learn the parameter of a constructed Bayesian network with a complete learning dataset, the default learning algorithm is still EM algorithm with uniform distribution. How does the EM algorithm deal with the complete dataset? I found the result is slightly different from the one estimated by Maximum Likelihood Estimation, which I originally expect the results will be the same.

marek [BayesFusion]
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Re: About Parameter Learning

Post by marek [BayesFusion] » Sat Dec 01, 2018 4:09 am

The EM algorithm replaces all missing values by the most likely values given the values of all the other variables and the current model and then learns the network parameters using MLE. It repeats this process until the change between the values of the parameters between two subsequent runs is smaller than a pre-defined threshold. When the data file contains no missing values, the algorithm deteriorates to MLE, so for a data set with no missing values, EM should result in learning similar parameters.
I hope this helps,


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