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Cover: 9781441923226 | All of Statistics | A Concise Course in Statistical Inference | Buch
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All of Statistics
A Concise Course in Statistical Inference
Taschenbuch von Larry Wasserman
Sprache: Englisch

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Beschreibung
Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines.
The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.
Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines.
The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.
Über den Autor
Larry Wasserman is Professor of Statistics at Carnegie Mellon University. He is also a member of the Center for Automated Learning and Discovery in the School of Computer Science. His research areas include nonparametric inference, asymptotic theory, causality, and applications to astrophysics, bioinformatics, and genetics. He is the 1999 winner of the Committee of Presidents of Statistical Societies Presidents' Award and the 2002 winner of the Centre de recherches mathematiques de Montreal-Statistical Society of Canada Prize in Statistics. He is Associate Editor of
The Journal of the American Statistical Association
and
The Annals of Statistics
. He is a fellow of the American Statistical Association and of the Institute of Mathematical Statistics.
Zusammenfassung
This book surveys a broad range of topics in probability and
mathematical statistics. It provides the statistical background that a
computer scientist needs to work in the area of machine learning.
Inhaltsverzeichnis
Probability.- Random Variables.- Expectation.- Inequalities.- Convergence of Random Variables.- Models, Statistical Inference and Learning.- Estimating the CDF and Statistical Functionals.- The Bootstrap.- Parametric Inference.- Hypothesis Testing and p-values.- Bayesian Inference.- Statistical Decision Theory.- Linear and Logistic Regression.- Multivariate Models.- Inference about Independence.- Causal Inference.- Directed Graphs and Conditional Independence.- Undirected Graphs.- Loglinear Models.- Nonparametric Curve Estimation.- Smoothing Using Orthogonal Functions.- Classification.- Probability Redux: Stochastic Processes.- Simulation Methods.
Details
Erscheinungsjahr: 2010
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Reihe: Springer Texts in Statistics
Inhalt: xx
442 S.
95 s/w Illustr.
ISBN-13: 9781441923226
ISBN-10: 1441923225
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Wasserman, Larry
Hersteller: Springer US
Springer New York
Springer US, New York, N.Y.
Springer Texts in Statistics
Maße: 234 x 156 x 26 mm
Von/Mit: Larry Wasserman
Erscheinungsdatum: 01.12.2010
Gewicht: 0,705 kg
Artikel-ID: 107253099
Über den Autor
Larry Wasserman is Professor of Statistics at Carnegie Mellon University. He is also a member of the Center for Automated Learning and Discovery in the School of Computer Science. His research areas include nonparametric inference, asymptotic theory, causality, and applications to astrophysics, bioinformatics, and genetics. He is the 1999 winner of the Committee of Presidents of Statistical Societies Presidents' Award and the 2002 winner of the Centre de recherches mathematiques de Montreal-Statistical Society of Canada Prize in Statistics. He is Associate Editor of
The Journal of the American Statistical Association
and
The Annals of Statistics
. He is a fellow of the American Statistical Association and of the Institute of Mathematical Statistics.
Zusammenfassung
This book surveys a broad range of topics in probability and
mathematical statistics. It provides the statistical background that a
computer scientist needs to work in the area of machine learning.
Inhaltsverzeichnis
Probability.- Random Variables.- Expectation.- Inequalities.- Convergence of Random Variables.- Models, Statistical Inference and Learning.- Estimating the CDF and Statistical Functionals.- The Bootstrap.- Parametric Inference.- Hypothesis Testing and p-values.- Bayesian Inference.- Statistical Decision Theory.- Linear and Logistic Regression.- Multivariate Models.- Inference about Independence.- Causal Inference.- Directed Graphs and Conditional Independence.- Undirected Graphs.- Loglinear Models.- Nonparametric Curve Estimation.- Smoothing Using Orthogonal Functions.- Classification.- Probability Redux: Stochastic Processes.- Simulation Methods.
Details
Erscheinungsjahr: 2010
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Reihe: Springer Texts in Statistics
Inhalt: xx
442 S.
95 s/w Illustr.
ISBN-13: 9781441923226
ISBN-10: 1441923225
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Wasserman, Larry
Hersteller: Springer US
Springer New York
Springer US, New York, N.Y.
Springer Texts in Statistics
Maße: 234 x 156 x 26 mm
Von/Mit: Larry Wasserman
Erscheinungsdatum: 01.12.2010
Gewicht: 0,705 kg
Artikel-ID: 107253099
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