11 Nov 2016
The

Expectation-Maximization (EM)
algorithm
is a popular method to obtain the

Maximum Likelihood Estimate
(MLE) when
some of the data may be missing. See [

Roche,
2012] for a nice tutorial on EM. The
following problem is a nice exercise in working out the algorithm
details to reinforce the concepts.

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29 Oct 2016
Assume that we are given a matrix .
Each row of the matrix is considered to be an observation
represented by a data vector that measures features of some
phenomenon. We can think of Principal Component Analysis (PCA) as trying
to trying to solve two related problems.

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12 Oct 2016
Thanks to

Prof. Larry for this problem!
Consider the following binary classification problem. Every individual
of a population is associated with an independent replicate of the pair
, having known joint distribution and where the
(observed) covariate has a (marginal) distribution
, and the (unobserved) response . Suppose
the costs of misclassifying an individual with and are and , respectively. Whatâ€™s the Bayes
decision rule?

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