Ebook: Advances in Probabilistic Graphical Models

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.

---

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.

---

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.

Content:
Front Matter....Pages I-X
Front Matter....Pages I-X
Markov Equivalence in Bayesian Networks....Pages 3-38
A Causal Algebra for Dynamic Flow Networks....Pages 39-54
Graphical and Algebraic Representatives of Conditional Independence Models....Pages 55-80
Bayesian Network Models with Discrete and Continuous Variables....Pages 81-102
Sensitivity Analysis of Probabilistic Networks....Pages 103-124
Front Matter....Pages I-X
A Review on Distinct Methods and Approaches to Perform Triangulation for Bayesian Networks....Pages 127-152
Decisiveness in Loopy Propagation....Pages 153-173
Lazy Inference in Multiply Sectioned Bayesian Networks Using Linked Junction Forests....Pages 175-190
Front Matter....Pages I-X
A Study on the Evolution of Bayesian Network Graph Structures....Pages 193-213
Learning Bayesian Networks with an Approximated MDL Score....Pages 215-234
Learning of Latent Class Models by Splitting and Merging Components....Pages 235-251
Front Matter....Pages I-X
An Efficient Exhaustive Anytime Sampling Algorithm for Influence Diagrams....Pages 255-273
Multi-currency Influence Diagrams....Pages 275-294
Parallel Markov Decision Processes....Pages 295-309
Front Matter....Pages I-X
Applications of HUGIN to Diagnosis and Control of Autonomous Vehicles....Pages 313-332
Biomedical Applications of Bayesian Networks....Pages 333-358
Learning and Validating Bayesian Network Models of Gene Networks....Pages 359-375
The Role of Background Knowledge in Bayesian Classification....Pages 377-396

---

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.

Content:
Front Matter....Pages I-X
Front Matter....Pages I-X
Markov Equivalence in Bayesian Networks....Pages 3-38
A Causal Algebra for Dynamic Flow Networks....Pages 39-54
Graphical and Algebraic Representatives of Conditional Independence Models....Pages 55-80
Bayesian Network Models with Discrete and Continuous Variables....Pages 81-102
Sensitivity Analysis of Probabilistic Networks....Pages 103-124
Front Matter....Pages I-X
A Review on Distinct Methods and Approaches to Perform Triangulation for Bayesian Networks....Pages 127-152
Decisiveness in Loopy Propagation....Pages 153-173
Lazy Inference in Multiply Sectioned Bayesian Networks Using Linked Junction Forests....Pages 175-190
Front Matter....Pages I-X
A Study on the Evolution of Bayesian Network Graph Structures....Pages 193-213
Learning Bayesian Networks with an Approximated MDL Score....Pages 215-234
Learning of Latent Class Models by Splitting and Merging Components....Pages 235-251
Front Matter....Pages I-X
An Efficient Exhaustive Anytime Sampling Algorithm for Influence Diagrams....Pages 255-273
Multi-currency Influence Diagrams....Pages 275-294
Parallel Markov Decision Processes....Pages 295-309
Front Matter....Pages I-X
Applications of HUGIN to Diagnosis and Control of Autonomous Vehicles....Pages 313-332
Biomedical Applications of Bayesian Networks....Pages 333-358
Learning and Validating Bayesian Network Models of Gene Networks....Pages 359-375
The Role of Background Knowledge in Bayesian Classification....Pages 377-396
....