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020 _a9783319644103
_9978-3-319-64410-3
040 _aCO-CtgIUMC
_bspa
_ccoctgiumc
_drda
082 0 4 _a005.55
_bF735
_223
100 1 _aForsyth, David.,
_eautor.
_4aut.
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aProbability and Statistics for Computer Science
_h[electronic resource] /
_cby David Forsyth.
264 4 _aCham : :
_bSpringer International Publishing : :
_bImprint: Springer,,
_c2018
264 1 _c2018
300 _aXXIV, 367 páginas. 124 illus., 84 illus. in color. :
_bonline resource.
336 _atexto
_btxt
_2rdacontent
337 _acomputador
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
504 _aIncluye referencias bibliográficas e índice.
505 0 _a1 Notation and conventions -- 2 First Tools for Looking at Data -- 3 Looking at Relationships -- 4 Basic ideas in probability -- 5 Random Variables and Expectations -- 6 Useful Probability Distributions -- 7 Samples and Populations -- 8 The Significance of Evidence -- 9 Experiments -- 10 Inferring Probability Models from Data -- 11 Extracting Important Relationships in High Dimensions -- 12 Learning to Classify -- 13 Clustering: Models of High Dimensional Data -- 14 Regression -- 15 Markov Chains and Hidden Markov Models -- 16 Resources.
520 _aThis textbook is aimed at computer science undergraduates late in sophomore or early in junior year, supplying a comprehensive  background in qualitative and quantitative data analysis, probability, random variables, and statistical methods, including machine learning. With careful treatment of topics that fill the curricular needs for the course, Probability and Statistics for Computer Science  features: •     A treatment of random variables and expectations dealing primarily with the discrete case. •     A  practical treatment of simulation, showing how many interesting probabilities and expectations can be extracted, with particular emphasis on  Markov chains. •     A clear but crisp account of simple point inference strategies (maximum likelihood;   Bayesian inference) in simple contexts. This is extended to cover some confidence intervals, samples and populations for random sampling with replacement, and the simplest hypothesis testing. •     A chapter dealing with classification, explaining why it's useful; how to train SVM classifiers with stochastic gradient descent; and how to use implementations of more advanced methods  such as  random forests and nearest neighbors. •     A chapter dealing with regression, explaining how to set up, use and understand linear regression and nearest neighbors regression in practical problems. •     A chapter dealing with principal components analysis, developing intuition carefully, and including numerous practical examples. There is a brief description of multivariate scaling via principal coordinate analysis. •     A chapter dealing with clustering via agglomerative methods and k-means, showing how to build vector quantized features for complex signals. Illustrated throughout, each main chapter includes many worked examples and other pedagogical elements such as boxed Procedures, Definitions, Useful Facts, and Remember This (short tips). Problems and Programming Exercises are at the end of each chapter, with a summary of what the reader should know.   Instructor resources include  a full set of model solutions for  all  problems, and an Instructor's Manual with accompanying presentation slides.
650 0 _aCiencias de la computación.
650 0 _aSimulación en computadores.
650 0 _aEstadística matemática.
650 1 4 _aProbabilidad en ciencias de la computación.
650 2 4 _aSimulación y modelación.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
856 _zDar click aqui para ver texto completo
942 _cCF
999 _c87775
_d87775