The EM Algorithm

The Expectation-Maximization (EM) algorithm is a broadly applicable approach to the iterative computation of maximum likelihood (ML) estimates, useful in a variety of incomplete-data problems. Maximum likelihood estimation and likelihood-based inference are of central importance in statistical theory and data analysis. Maximum likelihood estimation is a general-purpose method with attractive properties. It is the most-often used estimation technique in the frequentist framework; it is also relevant in the Bayesian framework (Chapter III.11). Often Bayesian solutions are justified with the help of likelihoods and maximum likelihood estimates (MLE), and Bayesian solutions are similar to penalized likelihood estimates. Maximum likelihood estimation is an ubiquitous technique and is used extensively in every area where statistical techniques are used. --

Cutaneous angiosarcoma of the scalp mimicking a keratoacanthoma

Cutaneous angiosarcoma (CA) has a wide range of clinical presentations. In this case report, we discuss a 78-year-old gentleman, who presented with a keratoacanthoma-like scalp lesion that turned out histologically to be a cutaneous angiosarcoma. A brief overview of CA, including its etiology, prognostic factors, clinical manifestations, and treatment options will also be discussed