This third edition covers today's most commonly used techniques in computational statistics while maintaining the same philosophy and writing style of the bestselling previous editions. This edition uses the latest version of MATLAB® and the corresponding version of the Statistics and Machine Learning Toolbox. It also incorporates new
This third edition covers today's most commonly used techniques in computational statistics while maintaining the same philosophy and writing style of the bestselling previous editions. This edition uses the latest version of MATLAB® and the corresponding version of the Statistics and Machine Learning Toolbox. It also incorporates new
Wendy L. Martinez is a mathematical statistician with the U.S. Bureau of Labor Statistics. She is a fellow of the American Statistical Association, a co-author of several popular Chapman & Hall/CRC books, and a MATLAB® user for more than 20 years. Her research interests include text data mining, probability density estimation, signal processing, scientific visualization, and statistical pattern recognition. She earned an M.S. in aerospace engineering from George Washington University and a Ph.D. in computational sciences and informatics from George Mason University. Angel R. Martinez is fully retired after a long career with the U.S. federal government and as an adjunct professor at Strayer University, where he taught undergraduate and graduate courses in statistics and mathematics. Before retiring from government service, he worked for the U.S. Navy as an operations research analyst and a computer scientist. He earned an M.S. in systems engineering from the Virginia Polytechnic Institute and State University and a Ph.D. in computational sciences and informatics from George Mason University.
Inhaltsangabe
Introduction. Probability Concepts. Sampling Concepts. Generating Random Variables. Exploratory Data Analysis. Finding Structure. Monte Carlo Methods for Inferential Statistics. Data Partitioning. Probability Density Estimation. Supervised Learning. Unsupervised Learning. Parametric Models. Nonparametric Models. Markov Chain Monte Carlo Methods. Appendices. References. Index.
Introduction. Probability Concepts. Sampling Concepts. Generating Random Variables. Exploratory Data Analysis. Finding Structure. Monte Carlo Methods for Inferential Statistics. Data Partitioning. Probability Density Estimation. Supervised Learning. Unsupervised Learning. Parametric Models. Nonparametric Models. Markov Chain Monte Carlo Methods. Appendices. References. Index.
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