Decision-theoretic methods for learning probabilistic models

Statistical learning, in particular, probabilistic model learning, has becomeincreasingly important in recent years. Probabilistic models, however, are not usually studied for their own sake but for decision-making purposes. Writtenby authorities in the field, Decision-Theoretic Methods for Learning Probabilistic Models approaches probabilistic models from the point of view of decision makers who operate in uncertain environments, base their decisions on a probabilistic model, and build and assess this model accordingly. This book surveys popular approaches and presents a review of utility theory. It also examinesapplications to finance, marketing, bioinformatics, and other fields.

• ISBN: 978-1-58488-622-8
• Editorial: CHAPMAN & HALL/CRC STATISTICS
• Encuadernacion: Cartoné
• Páginas: 401
• Fecha Publicación: 11/08/2010
• Nº Volúmenes: 1
• Idioma: Inglés