However, unlike the well-studied mcmc methodology, … KL divergence is a measure of how one probability distribution differs (in our case q) from the reference probability distribution (in our case p). (PDF) On the Properties of Kullback-Leibler Divergence Between … Donate to arXiv. 2.3 a Newton’s method to convert numerically a moment parameter to its corresponding natural parameter. I am comparing my results to these, but I can't reproduce their result. I wonder where I am doing a mistake and ask if anyone can spot it. However, that interpretation may make the KL divergence possibly more intuitive to understand. How to Calculate the KL Divergence for Machine Learning The KL Divergence is a measure of the dissimilarity between a ‘true’ distribution and a ‘prediction’ distribution. The ‘true’ distribution, p (x), is taken as fixed and the ‘prediction’ distribution, q (x), is controllable by us. KL So, I decided to investigate it to get a better intuition.

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