Bayesian networks currently provide one of the most rapidly growing areas of research in computer science and statistics. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. Each of the twelve chapters is self-contained. Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook forGraduate students since it shows the direction of current research Presents the innovative paradigms related to the theory and practical applications of Bayesian Networks INDICE: Introduction to Bayesian Networks.- A Polemic for Bayesian Statistics.- A Tutorial on Learning with Bayesian Networks.- The Causal Interpretation of Bayesian Networks.- An Introduction to Bayesian Networks and their Contemporary Applications.- Objective Bayesian Nets for Systems Modeling and Prognosis in Breast Cancer.- Predicting Epi Curves Using a Bayesian Network.- An Information-geometric Approach to Learning Bayesian Network Topologies from Data.-Causal Graphical Models with Latent Variables: Learning and Inference.- Use of Explanation Trees to describe the state space of a probabilistic-based abduction problem.- Maximum Entropy for inference in a class of multiply-connected Bayesian Networks.- A Survey of First-Order Probabilistic Models.
- ISBN: 978-3-540-85065-6
- Editorial: Springer
- Encuadernacion: Cartoné
- Páginas: 320
- Fecha Publicación: 01/09/2008
- Nº Volúmenes: 1
- Idioma: Inglés