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- 2011035553 contributor B12141578.
- 2011035553 created "2011.".
- 2011035553 date "2011".
- 2011035553 date "2011.".
- 2011035553 dateCopyrighted "2011.".
- 2011035553 description "Includes bibliographical references and index.".
- 2011035553 description "Machine generated contents note: Preface; Part I. Inference in Probabilistic Models: 1. Probabilistic reasoning; 2. Basic graph concepts; 3. Belief networks; 4. Graphical models; 5. Efficient inference in trees; 6. The junction tree algorithm; 7. Making decisions; Part II. Learning in Probabilistic Models: 8. Statistics for machine learning; 9. Learning as inference; 10. Naive Bayes; 11. Learning with hidden variables; 12. Bayesian model selection; Part III. Machine Learning: 13. Machine learning concepts; 14. Nearest neighbour classification; 15. Unsupervised linear dimension reduction; 16. Supervised linear dimension reduction; 17. Linear models; 18. Bayesian linear models; 19. Gaussian processes; 20. Mixture models; 21. Latent linear models; 22. Latent ability models; Part IV. Dynamical Models: 23. Discrete-state Markov models; 24. Continuous-state Markov models; 25. Switching linear dynamical systems; 26. Distributed computation; Part V. Approximate Inference: 27. Sampling; 28. Deterministic approximate inference; Appendix. Background mathematics; Bibliography; Index.".
- 2011035553 extent "xxiv, 697 p. :".
- 2011035553 identifier "9780521518147".
- 2011035553 identifier 9780521518147.jpg.
- 2011035553 identifier 2011035553-b.html.
- 2011035553 identifier 2011035553-d.html.
- 2011035553 identifier 2011035553-t.html.
- 2011035553 issued "2011".
- 2011035553 issued "2011.".
- 2011035553 language "eng".
- 2011035553 publisher "Cambridge ; New York : Cambridge University Press,".
- 2011035553 subject "006.3/1 23".
- 2011035553 subject "Bayesian statistical decision theory.".
- 2011035553 subject "COMPUTERS / Computer Vision & Pattern Recognition. bisacsh".
- 2011035553 subject "Machine learning.".
- 2011035553 subject "QA267 .B347 2011".
- 2011035553 tableOfContents "Machine generated contents note: Preface; Part I. Inference in Probabilistic Models: 1. Probabilistic reasoning; 2. Basic graph concepts; 3. Belief networks; 4. Graphical models; 5. Efficient inference in trees; 6. The junction tree algorithm; 7. Making decisions; Part II. Learning in Probabilistic Models: 8. Statistics for machine learning; 9. Learning as inference; 10. Naive Bayes; 11. Learning with hidden variables; 12. Bayesian model selection; Part III. Machine Learning: 13. Machine learning concepts; 14. Nearest neighbour classification; 15. Unsupervised linear dimension reduction; 16. Supervised linear dimension reduction; 17. Linear models; 18. Bayesian linear models; 19. Gaussian processes; 20. Mixture models; 21. Latent linear models; 22. Latent ability models; Part IV. Dynamical Models: 23. Discrete-state Markov models; 24. Continuous-state Markov models; 25. Switching linear dynamical systems; 26. Distributed computation; Part V. Approximate Inference: 27. Sampling; 28. Deterministic approximate inference; Appendix. Background mathematics; Bibliography; Index.".
- 2011035553 title "Bayesian reasoning and machine learning / David Barber.".
- 2011035553 type "text".
