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Foundations of statistical algorithms

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"Reviewing the historical development of basic algorithms to illuminate the evolution of today's more powerful statistical algorithms, this comprehensive textbook emphasizes recurring themes in all statistical algorithms including computation, assessment and verification, iteration, intuition, randomness, repetition and parallelization, and scalability. Unique in scope, it touches on topics not usually covered in  Read more...

Abstract: "Reviewing the historical development of basic algorithms to illuminate the evolution of today's more powerful statistical algorithms, this comprehensive textbook emphasizes recurring themes in all statistical algorithms including computation, assessment and verification, iteration, intuition, randomness, repetition and parallelization, and scalability. Unique in scope, it touches on topics not usually covered in similar books, namely, systematic verification and the scaling of many established techniques to very large databases. Broadly accessible, it offers examples, exercises, and selected solutions in each chapter as well as access to a supplementary website."

Author(s): Ligges, Uwe; Mersmann, Olaf; Weihs, Claus
Series: Series in computer science and data analysis
Publisher: CRC Press
Year: 2014

Language: English
Tags: Statistics -- Data processing.;Algorithms.;MATHEMATICS -- Probability & Statistics -- General.

Content: Introduction Computation
Motivation and History
Models for Computing: What Can a Computer Compute?
Floating-Point Computations: How Does a Computer Compute?
Precision of Computations: How Exact Does a Computer Compute?
Implementation in R Verification
Motivation and History
Theory
Practice and Simulation
Implementation in R Iteration
Motivation
Preliminaries
Univariate Optimization
Multivariate Optimization
Example: Neural Nets
Constrained Optimization
Evolutionary Computing
Implementation in R Deduction of Theoretical Properties
PLS—from Algorithm to Optimality
EM Algorithm
Implementation in R Randomization
Motivation and History
Theory: Univariate Randomization
Theory: Multivariate Randomization
Practice and Simulation: Stochastic Modeling
Implementation in R Repetition
Motivation and Overview
Model Selection
Model Selection in Classification
Model Selection in Continuous Models
Implementation in R Scalability and Parallelization
Introduction
Motivation and History
Optimization
Parallel Computing
Implementation in R Bibliography Index Conclusion and Exercises appear at the end of each chapter.