Focusing on numerical analysis relevant to statisticians, this volume equips students with the skills to develop their own software while exploring the benefits and limitations of numerical methods. Key topics covered include numerical stability, accurate approximations, and computational complexity, providing a comprehensive understanding of the subject.
"Applied Probability" uniquely combines theory and applications, focusing on mathematical modeling and computational techniques, particularly in biological sciences. It's suitable for graduate students in various fields and covers essential topics like stochastic processes, combinatorics, and optimization. The second edition includes new chapters and expanded exercises, making it a valuable reference for mathematical scientists.
Lange is a Springer author of other successful books. This is the first book that emphasizes the applications of optimization to statistics. The emphasis on statistical applications will be especially appealing to graduate students of statistics and biostatistics.
"Mathematical and Statistical Methods for Genetic Analysis" equips students in mathematical sciences to model epidemiological data in genetics. It covers principles applied to population genetics, gene mapping, and molecular evolution. The expanded second edition includes new topics like DNA analysis, Bayesian estimation, and case-control studies, along with numerous problems.
This tutorial presents a brief survey of semidifferentials in the familiar context of finite-dimensional Euclidean space. The main focus is on Hadamard semidifferentials. The Hadamard semidifferential is more general than the Fréchet differential, which is now dominant in undergraduate mathematics education.