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This text is designed for a one-semester junior/senior/graduate-level calculus-based course on probability and statistics, aimed specifically at data science students (including computer science). In addition to calculus, the text assumes basic knowledge of matrix algebra and rudimentary computer programming.
This text is designed for a one-semester junior/senior/graduate-level calculus-based course on probability and statistics, aimed specifically at data science students (including computer science). In addition to calculus, the text assumes basic knowledge of matrix algebra and rudimentary computer programming.
Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award.
1. Basic Probability Models. 2. Discrete Random Variables. 3. Discrete Parametric Distribution Families. 4. Introduction to Discrete Markov Chains. 5. Continuous Probability Models. 6. The Family of Normal Distributions. 7. The Family of Exponential Distributions. 8. Random Vectors and Multivariate Distributions. 9. Statistics: Prologue. 10. Introduction to Confidence Intervals. 11. Introduction to Significance Tests. 12. General Statistical Estimation and Inference 13. Predictive Modeling
| Erscheinungsjahr: | 2019 |
|---|---|
| Fachbereich: | Wahrscheinlichkeitstheorie |
| Genre: | Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 412 |
| Inhalt: | Einband - flex.(Paperback) |
| ISBN-13: | 9781138393295 |
| ISBN-10: | 1138393290 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | Matloff, Norman |
| Hersteller: | Taylor & Francis Ltd |
| Maße: | 231 x 154 x 30 mm |
| Von/Mit: | Norman Matloff |
| Erscheinungsdatum: | 20.06.2019 |
| Gewicht: | 0,67 kg |
Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award.
1. Basic Probability Models. 2. Discrete Random Variables. 3. Discrete Parametric Distribution Families. 4. Introduction to Discrete Markov Chains. 5. Continuous Probability Models. 6. The Family of Normal Distributions. 7. The Family of Exponential Distributions. 8. Random Vectors and Multivariate Distributions. 9. Statistics: Prologue. 10. Introduction to Confidence Intervals. 11. Introduction to Significance Tests. 12. General Statistical Estimation and Inference 13. Predictive Modeling
| Erscheinungsjahr: | 2019 |
|---|---|
| Fachbereich: | Wahrscheinlichkeitstheorie |
| Genre: | Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 412 |
| Inhalt: | Einband - flex.(Paperback) |
| ISBN-13: | 9781138393295 |
| ISBN-10: | 1138393290 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | Matloff, Norman |
| Hersteller: | Taylor & Francis Ltd |
| Maße: | 231 x 154 x 30 mm |
| Von/Mit: | Norman Matloff |
| Erscheinungsdatum: | 20.06.2019 |
| Gewicht: | 0,67 kg |
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