By Kevin B. Korb
Updated and accelerated, Bayesian synthetic Intelligence, moment Edition presents a realistic and obtainable advent to the most recommendations, origin, and functions of Bayesian networks. It makes a speciality of either the causal discovery of networks and Bayesian inference approaches. Adopting a causal interpretation of Bayesian networks, the authors talk about using Bayesian networks for causal modeling. in addition they draw all alone utilized study to demonstrate quite a few functions of the technology.
New to the second one Edition
- New bankruptcy on Bayesian community classifiers
- New part on object-oriented Bayesian networks
- New part that addresses foundational issues of causal discovery and Markov blanket discovery
- New part that covers tools of comparing causal discovery programs
- Discussions of many universal modeling errors
- New purposes and case studies
- More assurance at the makes use of of causal interventions to appreciate and cause with causal Bayesian networks
Illustrated with actual case stories, the second one version of this bestseller keeps to hide the basis of Bayesian networks. It offers the weather of Bayesian community know-how, automatic causal discovery, and studying percentages from facts and indicates the best way to hire those applied sciences to advance probabilistic specialist systems.
The book’s site at www.csse.monash.edu.au/bai/book/book.html deals quite a few supplemental fabrics, together with instance Bayesian networks and knowledge units. teachers can e mail the authors for pattern suggestions to some of the difficulties within the text.
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Extra info for Bayesian Artificial Intelligence, Second Edition
5 The odds may be set at any ratio and may, or may not, have something to do with one’s probabilities. Bookies typically set odds for and against events at a slight discrepancy with their best estimate of the probabilities, for their profit lies in the difference between the odds for and against. While odds and probabilities may deviate, probabilities and fair odds O(·) are strictly interchangeable concepts. 4) 1 + O(h) Thus, if a coin is fair, the probability of heads is 1/2, so the odds in favor of heads are 1:1 (usually described as “50:50”).
The probability of a false positive is 2%. What is the (posterior) probability that the athlete did in fact use steroids, given the positive outcome of the test? 01 (call this option h ); if it isn’t double-headed, then it’s a fair coin (call this option h). , you can’t simply examine both sides of the coin). , what’s the maximum number of tosses until that happens? ) Two cab companies, the Blue and the Green, operate in a given city. Eighty-five percent of the cabs in the city are Blue; the remaining 15% are Green.
Being adult humans), this gives us 1,625,000 eligible males and as many females. If we simplify by supposing that they are all in heterosexual partnerships, that will introduce a slight bias in favor of innocence; if we also simplify by ignoring the possibility of people traveling in cars with friends, this will introduce a larger bias in favor of guilt. The two together give us 1,625,000 available couples, suggesting a prior probability of guilt of 1/1625000. 8% chance of innocence and so a very large probability of a nasty error in judgment.
Bayesian Artificial Intelligence, Second Edition by Kevin B. Korb