## Abstract

This chapter explores some alternatives to maximum likelihood estimation by the EM algorithm. **Newton’s method** and **scoring** usually converge faster than the EM algorithm. However, the trade-offs of programming ease, numerical stability, and speed of convergence are complex, and statistical geneticists should be fluent in a variety of numerical optimization techniques for finding maximum likelihood estimates. Outside the realm of maximum likelihood, Bayesian procedures have much to offer in small to moderate-sized problems. For those uncomfortable with pulling prior distributions out of thin air, **empirical Bayes** procedures can be an appealing compromise between classical and Bayesian methods. This chapter illustrates some of these well-known themes in the context of allele frequency estimation and linkage analysis.

## Keywords

Exponential Family Multinomial Distribution Dirichlet Distribution Observe Information Matrix Tandem Repeat Locus## Preview

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