Question

(c) 5 points possible (graded) The problem you encountered in part (b) is called separation . It occurs when the can be perfectly recovered by a linear classifier, i.e., when there is a such that In order to avoid this behavior, one option is to use a prior on . Let us investigate what happens if we assume that is drawn from a distribution, i.e., What is the joint log likelihood of this Bayesian model

Answer 1

We have that these are the key points given below

The Bayesian Theorem is a theorem that centers or basis its claim on the facts that the probability of an event occurring is based on Prior Knowledge that of situation or Prior factors of the Event.

Likelihood function is a function that take into account all statistical model and determine there best application given the statistical data given.

Bayesian Theorem

The Bayesian Theorem is a theorem that centers or basis its claim on the facts that the probability of an event occurring is based on Prior Knowledge that of situation or Prior factors of the Event

i'll explain further with an example

Given that when two unfair dice are thrown a 100 times the probability of getting the higher figure in the range of 6-8 , Therefore the Bayesian Theorem simply assumes that this range of 6-8 to be the highest for all 100 throws of two dice.

The Bayesian model is given  as

$B=Prior * Likelihood=Posterior * Marginal$

Bayesian model is a statistical model.

This leads us to the definition of the Likelihood function

Likelihood function is a function that take into account all statistical model and determine there best application given the statistical data given.

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