Effective Maximum a posteriori (MAP) Estimate in Machine Learning

I am borrowing an example from Tom Mitchell’s video lecture to share some ideas on how we can effectively and objectively use MAP. Here goes the equation for outcomes of coin flips where our coin may not be an ideal coin (that’s the only reason we are making an intelligent machine to find probabilistic outcomes):

θMAP = arg maxθ P(D|θ) P(θ) = (α1 + β1 – 1)  /  (α1 + β1 – 1) (α0 + β0 – 1)

How we choose β values from our previous knowledge of coin can have interesting facts.

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