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Bayesian Reasoning and Machine Learning
출판사 : Cambridge University Press
저 자 : Barber
ISBN : 9780521518147
발행일 : 2012-1
도서종류 : 외국도서
발행언어 : 영어
페이지수 : 650
판매가격 : 60,000원
판매여부 : 재고확인요망
주문수량 : [+]수량을 1개 늘입니다 [-]수량을 1개 줄입니다

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   Bayesian Reasoning and Machine Learning 목차
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.
   도서 상세설명   

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.

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