2003 BayesianArtificialIntelligence

Subject Headings: Bayesian Network, Bayesian Probability, Utility Function.


Cited By

~466 http://scholar.google.com/scholar?cites=11142519679078858436


Publishers Abstract:

  • With Bayesian network technology very much on the up-swing in industry and government, there is an increasing need for an introductory book that jointly emphasizes the understanding of its underlying priniciples and their application in practice. Bayesian Artificial Intelligence presents elements of Bayesian network technology, automated causal discovery, and learning probabilities from data along with extensive motivational examples of using these technologies to develop probabilistic expert systems. This practical, very accessible introduction balances the causal discovery of networks with the Bayesian inference procedures that use a network once it is found. The authors emphasize understanding and intuition, so they keep the mathematical details to a minimum, but also provide the algorithms and technical background needed for applications. They illustrate at length a number of applications and discuss application software in detail. A broad range of topics, a practical perspective, and a thoughtful discussion of philosophical underpinnings make Bayesian Artificial Intelligence an ideal introduction for students and for professionals who want to broaden their expertise. It provides the knowledge you need to put Bayesian network tools into practice, while also forming the basis for a more detailed investigation of the technology and original research.
  • Bayesianism is the philosophy that asserts that in order to understand human opinions as it ought to be, constrained by ignorance and uncertainty, the probability calculus is the single most important tool for representing appropriate strengths of belief.
  • Given a general ability to order situations, and bets with definite probabilities of yielding particular situations, Frank Ramsey [231] demonstrated that we can identify particular utilities with each possible situation, yielding a utility function. If we have a utility function U(Oi|A) over every possible outcome of a particular action [math]\displaystyle{ A }[/math] we are contemplating, and if we have a probability for each such otucome P(Oi|A), then we can compute the probability-weighted average utility for that action - otherwise known as the expected utility of the action. … It is commonly taken as axiomatic by Bayesians that agents ought to maximize their expected utility.
  • Bayesian networks (BNs) are graphical models for reasoning under uncertainty, where the nodes represent variables (discrete or continuous) and arcs represent direct connections between them. There direct connections are often causal connections. In addition, BNs model the quantitative strength of the connections between variables, allowing probabilistic beliefs about them to be updated automatically as new information becomes available. … The only constraint on the arcs allowed in a BN is that there must not be any directed cycles.
  • Structure terminology and layout. In talk about about network structure it is useful to employ a family metaphor: a node is a parent of a child, if there is an arc from the former to the latter. Extending the metaphor, if there is a directed chain of nodes, no node is an ancestor of another if it appears earlier in the chain, whereas a node is a descendant of another node if it is comes later in the chain. … Another useful concept is that of the Markov Blanket of a node, which consists of the node's parents, its children and its children's parents. .. Any node without parents is called a root node, while any node without children is called a leaf node. Any other node (non-leaf and non-root) is called an intermediate node.



 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2003 BayesianArtificialIntelligenceKevin B. Korb
Ann E. Nicholson
Bayesian Artificial Intelligencehttp://books.google.com/books?id=hFGsgaQhurIC