Bayesian Networks: An Introduction
โ Scribed by Timo Koski, John Noble
- Publisher
- Wiley
- Year
- 2009
- Tongue
- English
- Leaves
- 368
- Series
- Wiley Series in Probability and Statistics
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
Bayesian Networks: An Introduction provides a self-contained introduction to the theory and applications of Bayesian networks, a topic of interest and importance for statisticians, computer scientists and those involved in modelling complex data sets. The material has been extensively tested in classroom teaching and assumes a basic knowledge of probability, statistics and mathematics. All notions are carefully explained and feature exercises throughout.
Features include:
- An introduction to Dirichlet Distribution, Exponential Families and their applications.
- A detailed description of learning algorithms and Conditional Gaussian Distributions using Junction Tree methods.
- A discussion of Pearl's intervention calculus, with an introduction to the notion of see and do conditioning.
- All concepts are clearly defined and illustrated with examples and exercises. Solutions are provided online.
This book will prove a valuable resource for postgraduate students of statistics, computer engineering, mathematics, data mining, artificial intelligence, and biology.
Researchers and users of comparable modelling or statistical techniques such as neural networks will also find this book of interest.
๐ SIMILAR VOLUMES
This book really helps in bridging formalism to understanding by providing lots of examples and walking through the examples. It's a pleasure to read. One can skim what seems basic. But if something is not clear, one can work through a few examples. It's strength is pedagogical.
Bayesian Networks: An Introduction provides a self-contained introduction to the theory and applications of Bayesian networks, a topic of interest and importance for statisticians, computer scientists and those involved in modelling complex data sets. The material has been extensively tested in clas
Bayesian Networks: An Introduction provides a self-contained introduction to the theory and applications of Bayesian networks, a topic of interest and importance for statisticians, computer scientists and those involved in modelling complex data sets. The material has been extensively tested in cl
Bayesian Statistics is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs. The first edition of Peter Lee's book appeared in 1989, but the subject has moved ever onwards, with increasing emphasis on Monte Carlo based techniques. This