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Bayesian reasoning and machine learning
La 4e de couverture indique : "Machine learning methods extract value from vast data sets and are established tools in a wide range of business, industrial and scientific applications. This introduction for final-year undergraduate and graduate students conveys the basic computational reasoning...
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| Auteur principal : | |
|---|---|
| Format : | Livre |
| Langue : | anglais |
| Titre complet : | Bayesian reasoning and machine learning / David Barber,... |
| Publié : |
Cambridge :
Cambridge University Press
, copyright 2012 |
| Description matérielle : | 1 vol. (XXIV-697 p.) |
| Sujets : |
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| 200 | 1 | |a Bayesian reasoning and machine learning |f David Barber,... | |
| 210 | |a Cambridge |c Cambridge University Press |d copyright 2012 | ||
| 215 | |a 1 vol. (XXIV-697 p.) |c ill. en noir et en coul., couv. en coul. |d 26 cm | ||
| 305 | |a Autre tirage : 2015 (6e tirage), 2017 (9e), 2018 (10e) | ||
| 320 | |a Bibliogr. p. [675]-688. Index | ||
| 330 | |a La 4e de couverture indique : "Machine learning methods extract value from vast data sets and are established tools in a wide range of business, industrial and scientific applications. This introduction for final-year undergraduate and graduate students conveys the basic computational reasoning and more advanced techniques, giving students the insight and skills they need. For students without a firm background in statistics, calculus or linear algebra. Unified conceptual treatment within the framework of graphical models, Bayesian probability and graph theory. Students develop analytical and problem-solving skills needed for the real world. Numerous examples and exercises, both computer-based and theoretical. Downloadable MATLAB toolbox, with demos." | ||
| 359 | 2 | |p P. xv |b Preface |b Part I, Inference in Probabilistic Models |p P. 3 |c 1, Probabilistic reasoning |p P. 22 |c 2, Basic graph concepts |p P. 29 |c 3, Belief networks |p P. 58 |c 4, Graphical models |p P. 77 |c 5, Efficient inference in trees |p P. 102 |c 6, The junction tree algorithm |p P. 127 |c 7, Making decisions |b Part II, Learning in Probabilistic Models |p P. 165 |c 8, Statistics for machine learning |p P. 199 |c 9, Learning as inference |p P. 243 |c 10, Naive Bayes |p P. 256 |c 11, Learning with hidden variables |p P. 284 |c 12, Bayesian model selection |b Part III, Machine Learning |p P. 305 |c 13, Machine learning concepts |p P. 322 |c 14, Nearest neighbour classification |p P. 329 |c 15, Unsupervised linear dimension reduction |p P. 359 |c 16, Supervised linear dimension reduction |p P. 367 |c 17, Linear models |p P. 392 |c 18, Bayesian linear models |p P. 412 |c 19, Gaussian processes |p P. 432 |c 20, Mixture models |p P. 462 |c 21, Latent linear models |p P. 473 |c 22, Latent ability models |b Part IV, Dynamical Models |p P. 489 |c 23, Discrete-state Markov models |p P. 520 |c 24, Continuous-state Markov models |p P. 547 |c 25, Switching linear dynamical systems |p P. 568 |c 26, Distributed computation |b Part V, Approximate Inference |p P. 587 |c 27, Sampling |p P. 617 |c 28, Deterministic approximate inference |p P. 655 |b Appendix, Background mathematics |p P. 675 |b References |p P. 689 |b Index | |
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