Probability and statistics for computer science / David Forsyth.
Tipo de material:
TextoDetalles de publicación: Cham, Switzerland : Springer, 2018Edición: 1st edDescripción: xxiv, 367 p. : il. ; 28 cmISBN: 9783319877884; 9783319644103 e-bookTema(s): Computación -- Métodos estadísticosClasificación LoC:QA76.9.M35 | F67 2018
Contenidos:
1 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.
| Tipo de ítem | Biblioteca actual | Colección | Signatura | Copia número | Estado | Fecha de vencimiento | Código de barras |
|---|---|---|---|---|---|---|---|
Libro
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Biblioteca Central Ensenada | Acervo General | QA76.9.M35 F67 2018 (Browse shelf(Abre debajo)) | 1 | Prestado | 13/11/2024 | ENS097616 |
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| QA76.9 .M35 E86 2016 Matemáticas discretas / | QA76.9 .M35 E86 2016 Matemáticas discretas / | QA76.9 .M35 E86 2016 Matemáticas discretas / | QA76.9.M35 F67 2018 Probability and statistics for computer science / | QA76.9.M35 J45 2018 Fundamentals of discrete math for computer science : a problem-solving primer / | QA76.9 .M35 J55 2015 Matemáticas para la computación / | QA76.9.M35 J55 2015 Matemáticas para la computación / |
1 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.
