Note: Supplemental materials are not guaranteed with Rental or Used book purchases.
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What is included with this book?
Preface | |
Inference in Probabilistic Models | |
Probabilistic reasoning | |
Basic graph concepts | |
Belief networks | |
Graphical models | |
Efficient inference in trees | |
The junction tree algorithm | |
Making decisions | |
Learning in Probabilistic Models | |
Statistics for machine learning | |
Learning as inference | |
Naive Bayes | |
Learning with hidden variables | |
Bayesian model selection | |
Machine Learning | |
Machine learning concepts | |
Nearest neighbour classification | |
Unsupervised linear dimension reduction | |
Supervised linear dimension reduction | |
Linear models | |
Bayesian linear models | |
Gaussian processes | |
Mixture models | |
Latent linear models | |
Latent ability models | |
Dynamical Models | |
Discrete-state Markov models | |
Continuous-state Markov models | |
Switching linear dynamical systems | |
Distributed computation | |
Approximate Inference | |
Sampling | |
Deterministic approximate inference | |
Appendix. Background mathematics | |
Bibliography | |
Index | |
Table of Contents provided by Publisher. All Rights Reserved. |
The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.
The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.