Maximum Likelihood Estimate for Bernoulli Distribution | Derivation and Implementation

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In this video, we derive the Maximum Likelihood Estimate (MLE) for the Bernoulli Distribution. The MLE just depends on the dataset we are using and is prone to overfit. Nevertheless, it provides a good first estimate.

After the derivation, we will check our results using TensorFlow Probability.

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Timestamps:
0:00 Opening
0:13 Repetition Bernoulli
01:00 Problem Definition
01:48 Definition Likelihood
02:51 Log-Likelihood
06:13 Optimizing the Log-Likelihood
12:48 TensorFlow Probability
  1. Machine Learning & Simulation
  2. tfp
  3. tensorflow probability
  4. coin toss
  5. coin flip
  6. binary probability
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Комментарии
Автор

finnally some one that does it right and SHOWS where the 1/n at the end comes from there are soooo many videos where they just add it out of no where and it keeps messing up my thoughts and notes... ohhh i forgot you can cross multiply like that

joelcollazo
Автор

Should the likelihood function not be L(theta given data) but not L(data given theta) as in the video? Is that a mistake?

lawrence