Titlebook: Applied Probability; Kenneth Lange Textbook 20031st edition Springer Science+Business Media New York 2003 Analysis.Markov.Measure.Probabil

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https://doi.org/10.1007/0-306-48020-4pplied mathematics [., ., .]. Although diffusion models address and solve many interesting stochastic questions that are impossible to even discuss in a deterministic framework, these models also raise the mathematical bar. The current chapter surveys the theory at an elementary level, stressing int

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Basic Principles of Population Genetics,arious genetic definitions are recalled merely to provide a context for this and more advanced mathematical theory. Readers with a limited knowledge of modern genetics are urged to learn molecular genetics by formal course work or informal self-study. Appendix A summarizes a few of the major current

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Counting Methods and the EM Algorithm,dels, geneticists by and large rely on maximum likelihood estimators rather than on competing estimators derived from minimax, invariance, robustness, or Bayesian principles. A host of methods exists for numerically computing maximum likelihood estimates. Some of the most appealing involve simple co

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,Newton’s Method and Scoring, 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,

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Computation of Mendelian Likelihoods,vestigate efficient algorithms for likelihood computation on pedigree data, placing particular stress on the pioneering algorithm of Elston and Stewart [.]. It is no accident that their research coincided with the introduction of modern computing. To analyze human pedigree data is tedious, if not im

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