Bayesian Inference

  Bayesian Inference

Update probability with new information (data).

Combining two distributions (Likelihood and Prior) into Posterior.

Posterior is used find the “best” parameters in terms of maximizing the posterior probability.

Steps:

i.            Prior: Choose a PDF to model i.e. the prior distribution P(θ).

ii.            Likelihood: Choose a PDF for P(X|θ). How the data X will look like given the parameter θ.

iii.            Posterior: Calculate the posterior distribution P(θ|X) and pick the θ that has the highest P(θ|X).

 

Calculate P(θ) & P(X|θ) for a specific θ and multiply them together. Pick the highest P(θ) * P(X|θ) among different θ’s.

 Posterior becomes the new prior. Repeat step 3 as you get more data.