Posterior for the Bernoulli using the Conjugate Prior | with example in TensorFlow Probability

If we observe data on the event modelled by a Bernoulli distribution, we could be interested in finding a posterior distribution over the latent parameter to it. If we use a conjugate prior, this posterior has a closed-form solution. Here are the notes: https://raw.githubusercontent.com/Ceyron/machine-learning-and-simulation/main/english/essential_pmf_pdf/bernoulli_posterior.pdf

The Bernoulli distribution is actually one of these rare cases in which we can actually express all associated distributions: The marginal, the posterior and the predictive posterior. Other more sophisticated distributions do not allow for this since we there run into the trouble of intractability when applying Bayes' rule.

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Timestamps
00:00 Opening
00:16 Task of inferring parameters from data
01:30 Graphical Model and joint
04:10 Deriving the Posterior
11:20 A conjugate prior
11:55 TensorFlow Probability
15:25 End-Card

Видео Posterior for the Bernoulli using the Conjugate Prior | with example in TensorFlow Probability канала Machine Learning & Simulation