Using the fact that the total area underneath a probability density function
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Using the fact that the total area underneath a probability density function

4. Maximum likelihood meets histograms

Let X1, X2, · · · , Xn be n i.i.d data points drawn from a piece-wise constant probability density function over N equal size bins between 0 and 1 (B1, B2, · · · , BN ), where the constants are θ1, θ2, · · · , θN .


(a) Using the fact that the total area underneath a probability density function is 1, express θN in terms of the other constants.

(b) Write down the log-likelihood of the data in terms of θ1, θ2, · · · , θN−1 and µ1, µ2, · · · , µN−1.

(c) Find the maximum likelihood estimate of θj for j ∈ {1, 2, · · · , N}.

Hint
StatisticsMLE (Maximum Likelihood Estimation) is a method for estimating a model's parameters. This is one of the most commonly used estimate methods. The maximum likelihood approach chooses the collection of model parameter values that maximizes the likelihood function. ...

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