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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 : |
- P. xv
- Preface
- Part I, Inference in Probabilistic Models
- P. 3
- 1, Probabilistic reasoning
- P. 22
- 2, Basic graph concepts
- P. 29
- 3, Belief networks
- P. 58
- 4, Graphical models
- P. 77
- 5, Efficient inference in trees
- P. 102
- 6, The junction tree algorithm
- P. 127
- 7, Making decisions
- Part II, Learning in Probabilistic Models
- P. 165
- 8, Statistics for machine learning
- P. 199
- 9, Learning as inference
- P. 243
- 10, Naive Bayes
- P. 256
- 11, Learning with hidden variables
- P. 284
- 12, Bayesian model selection
- Part III, Machine Learning
- P. 305
- 13, Machine learning concepts
- P. 322
- 14, Nearest neighbour classification
- P. 329
- 15, Unsupervised linear dimension reduction
- P. 359
- 16, Supervised linear dimension reduction
- P. 367
- 17, Linear models
- P. 392
- 18, Bayesian linear models
- P. 412
- 19, Gaussian processes
- P. 432
- 20, Mixture models
- P. 462
- 21, Latent linear models
- P. 473
- 22, Latent ability models
- Part IV, Dynamical Models
- P. 489
- 23, Discrete-state Markov models
- P. 520
- 24, Continuous-state Markov models
- P. 547
- 25, Switching linear dynamical systems
- P. 568
- 26, Distributed computation
- Part V, Approximate Inference
- P. 587
- 27, Sampling
- P. 617
- 28, Deterministic approximate inference
- P. 655
- Appendix, Background mathematics
- P. 675
- References
- P. 689
- Index