Description Graphical models (e.
The target audience of this book is researchers and students in the artificial intelligence and machine learning area, and beyond..
We believe the principles outlined in the book would serve well in moving forward to approximation and anytime-based schemes.
The new Edition includes the notion of influence diagrams, which focus on sequential decision making under uncertainty.
We emphasize the dependence of both schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. space behavior.
Each class possesses distinguished characteristics and in particular has different time vs. g., cycle-cutset conditioning and AND/OR search). g., variable-elimination) and search-based, conditioning schemes (e.
We present inference-based, message-passing schemes (e.
The main feature exploited by the Algorithms is the model\'s graph.
This book provides comprehensive coverage of the primary Exact Algorithms for Reasoning with such models.
It is well known that the tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques that significantly advanced the state of the art.
These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and Probabilistic inference.
These models are used to perform many Reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and Reasoning in both artificial intelligence and computer science in general.
Description Graphical models (e
