04772nam a22007335i 4500 000717959 DE-He213 20240923053748.0 20240313095821.0 cr nn 008mamaa 131129s2004 xxu| s |||| 0|eng d NEZPRACOVANÝ IMPORT 9780387217369 978-0-387-21736-9 10.1007/978-0-387-21736-9 doi QA71-90 PBKS bicssc MAT041000 bisacsh PBKS thema 518 23 Wasserman, Larry. author. aut http://id.loc.gov/vocabulary/relators/aut All of Statistics [elektronický zdroj] : A Concise Course in Statistical Inference / by Larry Wasserman. 1st ed. 2004. New York, NY : Springer New York : Imprint: Springer, 2004. XX, 442 p. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Springer Texts in Statistics, 2197-4136 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. This book is for people who want to learn probability and statistics quickly. It brings together many of the main ideas in modern statistics in one place. The book is suitable for students and researchers in statistics, computer science, data mining and machine learning. This book covers a much wider range of topics than a typical introductory text on mathematical statistics. It includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses. The reader is assumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. The text can be used at the advanced undergraduate and graduate level. 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. elektronické knihy fd186907 czenas electronic books eczenas Mathematics Data processing. Probabilities. System theory. Statistics . Computer science Mathematics. Mathematical statistics. Computational Mathematics and Numerical Analysis. Probability Theory. Complex Systems. Statistical Theory and Methods. Probability and Statistics in Computer Science. Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. SpringerLink (Online service) Springer Nature eBook Printed edition: 9780387402727 Printed edition: 9781441923226 Printed edition: 9781468495522 Springer Texts in Statistics, 2197-4136 https://zdroje.vse.cz/login?url=https://doi.org/10.1007/978-0-387-21736-9 Plný text SPRINGER-OWNED EB PhDinfra PAR 000099596 UEP01 776 E-book Tištěná verze NEPOSILAT EB ZDB-2-SMA ZDB-2-SXMS ZDB-2-BAE Mathematics and Statistics (SpringerNature-11649) Mathematics and Statistics (R0) (SpringerNature-43713)