Approximation methods for efficient learning of Bayesian networks / Carsten Riggelsen.
Material type:
TextSeries: Frontiers in artificial intelligence and applications ; v 168. | Dissertations in artificial intelligencePublication details: Amsterdam : IOS Press, ©2008.Description: vii, 137 p. : ill. ; 25 cmISBN: - 9781586038212
- 000SA.161 23 R569
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 000SA.161 R569 (Browse shelf(Opens below)) | Available | 137402 |
Browsing ISI Library, Kolkata shelves Close shelf browser (Hides shelf browser)
| 000SA.161 N147 Bayesian networks in R : | 000SA.161 P532 Prior processes and their applications : | 000SA.161 P852 Modeling and analysis of dependable systems : | 000SA.161 R569 Approximation methods for efficient learning of Bayesian networks / | 000SA.161 Sa245 Bayesian filtering and smoothing / | 000SA.161 Sc437 Bayesian networks : | 000SA.161 T473 Bayesian analysis with Stata / |
Thesis (Ph.D.)--Utrecht University, 2006.
Includes bibliographical references.
1. Introduction;
2. Preliminaries;
3. Learning Bayesian Networks from Data;
4. Monte Carlo Methods and MCMC Simulation;
5. Learning from Incomplete Data;
6. Conclusion.
This book offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. This publication discusses basic concepts about probabilities, graph theory and conditional independence; and Bayesian network learning from data.
There are no comments on this title.
