EM Algorithm Derivation

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  1. DeepSchoolAi
  2. Machine Learning
  3. EM algorithm
  4. Maximum Likelihood
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Комментарии
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by far the best and simplest explanation of EM

AyanDasIEMECE
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After watching this video finally got some clarity about EM Algorithm thank you very

dineshnalam
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Thank you. q technically can be ANY probability distribution. However, only few actually can minimize the second term. If P(z|x) is analytical you let q(z)=P(z|x), otherwise, you would probably have to do some Variational Bayes (will do a video on this on a later date).

deepschoolai
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Nice video. I am just wondring how we do select the function q.

sirusThu
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best explanation i have seen. thanks!

orjihvy
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Great video, and one of the shortest on youtube

danielawesome
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Is there more than one way to define KL? I have seen examples where p and q is the other way.

orjihvy
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thanks, this video is pretty straight and clear, AND SOOOO HELPFUL ;D

pengjl
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Whoops you are absolutely correct, will fix it

deepschoolai
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3:40 wait what? Which laws are you applying to get ln a - ln b - ln c + ln b = ln (a / b) - ln (c / b) ? Or probably rather ln a - ln b - ln c + ln b = ln (a) / b + ( - ln (b) / c) which I don't follow either.

a is P(x, z)
b is q(z)
c is P(z | x)

You can do of course ln a - ln b - ln c + ln b = ln (a / b) + ln (b / c), but your rewriting I don't understand.

ElizaberthUndEugen