Buch, Englisch, 386 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 616 g
Buch, Englisch, 386 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 616 g
Reihe: Studies in Fuzziness and Soft Computing
ISBN: 978-3-642-08854-4
Verlag: Springer
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Operations Research Graphentheorie
- Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
- Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
- Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computer-Aided Design (CAD)
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Numerische Mathematik
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
- Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
Weitere Infos & Material
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.
