TY  - BOOK
AU  - 
AU  - 
AU  - 
ED  - SpringerLink (Online service)
TI  - Advances in Probabilistic Graphical Models
T2  - Studies in Fuzziness and Soft Computing,
SN  - 9783540689966
AV  - TA329-348 
U1  - 519 23
PY  - 2007///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg
KW  - Engineering
KW  - Artificial intelligence
KW  - Engineering mathematics
KW  - Appl.Mathematics/Computational Methods of Engineering
KW  - Artificial Intelligence (incl. Robotics)
N1  - 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; ZDB-2-ENG
N2  - 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
UR  - http://dx.doi.org/10.1007/978-3-540-68996-6
ER  -