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Bayesian networks

Formula

Terminology

Probability

Laplace smoothibg

Nature of network

Intution

given P(B(A) , P (A) & P(B) ,the reverse condition probability is P(Ci/X) = P (X/Ci )P ( Ci)/ P(X)

P (Ci) - Prior probability P ( X /Ci) - Likelihood function P( Ci /X ) - Posterior probability

Joint probability - P(A intersect B) = P (A|B) P (B)* condition probability - P(A|B) = P (A intersect B )| P(B)

function updates prior beliefs with new information Posterior probability is final outcome .

It is used in discrete datasets These networks can be used for continuous variables

These network can slove the problem if inconclusive results from model by adding 1 to avoid any probability value of 0