The expectation-maximization (EM) algorithm is an elegant algorithm that maximizes the likelihood function for problems with latent or hidden variables. As from the name itself it could primarily be ...

Expectation Maximization is used to find the maximum likelihood of the model parameter when model depends on unobserved or latent variables. Expectation Maximization was proposed 1977 by a paper ...

The American Statistician strives to publish articles of general interest to the statistical profession on topics that are important for a broad group of statisticians, and ordinarily not highly ...

Will Kenton is an expert on the economy and investing laws and regulations. He previously held senior editorial roles at Investopedia and Kapitall Wire and holds a MA in Economics from The New School ...

The LHS indicates that g is a function of D variables whereas the RHS indicates that g is a function of only 1 variable.

Abstract: In this chapter, we introduce the concept of a random variable and develop the procedures for characterizing random variables, including the cumulative distribution function, as well as the ...

Abstract: Sums of exponentially distributed random variables (RVs) play important roles in performance analysis of various communication systems. Their logarithmic expectations can not only facilitate ...

Do the Expectation, assuming the input DiscreteFunction is a function only of the queried variable. void expectation(DiscreteFunction df, java.lang.String[] order) Do ...