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Advances in Probabilistic Graphical Models by Dr. Lucas, Peter: New
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Item specifics
- Condition
- Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See all condition definitionsopens in a new window or tab
- Book Title
- Advances in Probabilistic Graphical Models
- Publication Date
- 2007-02-05
- Pages
- 386
- ISBN
- 9783540689942
- Subject Area
- Mathematics, Computers
- Publication Name
- Advances in Probabilistic Graphical Models
- Publisher
- Springer Berlin / Heidelberg
- Item Length
- 9.3 in
- Subject
- Probability & Statistics / Stochastic Processes, Intelligence (Ai) & Semantics, Probability & Statistics / General, Neural Networks, Applied, Discrete Mathematics, Probability & Statistics / Bayesian Analysis
- Publication Year
- 2007
- Series
- Studies in Fuzziness and Soft Computing Ser.
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Weight
- 26.7 Oz
- Item Width
- 6.1 in
- Number of Pages
- X, 386 Pages
About this product
Product Identifiers
Publisher
Springer Berlin / Heidelberg
ISBN-10
354068994X
ISBN-13
9783540689942
eBay Product ID (ePID)
25038398178
Product Key Features
Number of Pages
X, 386 Pages
Language
English
Publication Name
Advances in Probabilistic Graphical Models
Publication Year
2007
Subject
Probability & Statistics / Stochastic Processes, Intelligence (Ai) & Semantics, Probability & Statistics / General, Neural Networks, Applied, Discrete Mathematics, Probability & Statistics / Bayesian Analysis
Type
Textbook
Subject Area
Mathematics, Computers
Series
Studies in Fuzziness and Soft Computing Ser.
Format
Hardcover
Dimensions
Item Weight
26.7 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2006-939264
Dewey Edition
22
Series Volume Number
213
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
519.5/42
Table Of Content
Foundations.- Markov Equivalence in Bayesian Networks.- A Causal Algebra for Dynamic Flow Networks.- Graphical and Algebraic Representatives of Conditional Independence Models.- Bayesian Network Models with Discrete and Continuous Variables.- Sensitivity Analysis of Probabilistic Networks.- Inference.- A Review on Distinct Methods and Approaches to Perform Triangulation for Bayesian Networks.- Decisiveness in Loopy Propagation.- Lazy Inference in Multiply Sectioned Bayesian Networks Using Linked Junction Forests.- Learning.- A Study on the Evolution of Bayesian Network Graph Structures.- Learning Bayesian Networks with an Approximated MDL Score.- Learning of Latent Class Models by Splitting and Merging Components.- Decision Processes.- An Efficient Exhaustive Anytime Sampling Algorithm for Influence Diagrams.- Multi-currency Influence Diagrams.- Parallel Markov Decision Processes.- Applications.- Applications of HUGIN to Diagnosis and Control of Autonomous Vehicles.- Biomedical Applications of Bayesian Networks.- Learning and Validating Bayesian Network Models of Gene Networks.- The Role of Background Knowledge in Bayesian Classification.
Synopsis
This book brings together important topics of current research in probabilistic graphical modeling, learning from data and probabilistic inference. Coverage includes such topics as the characterization of conditional independence, the learning of graphical models with latent variables, and extensions to the influence diagram formalism as well as important application fields, such as the control of vehicles, bioinformatics and medicine., In recent years considerable progress has been made in the area of probabilistic graphical models, in particular Bayesian networks and influence diagrams. Probabilistic graphical models have become mainstream in the area of uncertainty in artificial intelligence; contributions to the area are coming from computer science, mathematics, statistics and engineering. This carefully edited book brings together in one volume some of the most important topics of current research in probabilistic graphical modelling, learning from data and probabilistic inference. This includes topics such as the characterisation of conditional independence, the sensitivity of the underlying probability distribution of a Bayesian network to variation in its parameters, the learning of graphical models with latent variables and extensions to the influence diagram formalism. In addition, attention is given to important application fields of probabilistic graphical models, such as the control of vehicles, bioinformatics and medicine.
LC Classification Number
QA273.A1-274.9
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