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- Daniel James Lizotte, Isaac Asimov, I Robot, Russell Greiner, Peter Hooper, Robert Holte +1 other
- 2003

Permission is hereby granted to the University of Alberta Library to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein before provided, neither the… (More)

A Bayesian Belief Network (BN) is a model of a joint distribution over a finite set of variables, with a DAG structure to represent the immediate dependencies between the variables, and a set of parameters (aka CPTables) to represent the local conditional probabilities of a node, given each assignment to its parents. In many situations , the parameters are… (More)

A Bayesian belief network models a joint distribution over variables using a DAG to represent variable dependencies and network parameters to represent the conditional probability of each variable given an assignment to its immediate parents. Existing algorithms assume each network parameter is fixed. From a Bayesian perspective, however, these network… (More)

- Paul Ajit, Singh, Geoffrey J Gordon, Tom Mitchell, Christos Faloutsos, Pedro Domingos +13 others
- 2000

The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the U.S. government or any other entity. Abstract Relational learning deals with the setting where one has multiple sources of data, each describing… (More)

- Peter Hooper
- UAI
- 2004

A Bayesian belief network is a model of a joint distribution over a finite set of variables , with a DAG structure representing immediate dependencies among the variables. For each node, a table of parameters (CP-table) represents local conditional probabilities , with rows indexed by conditioning events (assignments to parents). CP-table rows are usually… (More)

A Bayesian belief network models a joint distribution with an directed acyclic graph representing dependencies among variables and network parameters characterizing conditional distributions. The parameters are viewed as random variables to quantify uncertainty about their values. Belief nets are used to compute responses to queries; i.e., conditional… (More)

- Eliezer, S Yudkowsky, Liuyang Li, Russell Greiner, Peter Hooper
- 2009

The Bayesian revolution in the sciences is fueled, not only by more and more cognitive scientists suddenly noticing that mental phenomena have Bayesian structure in them; not only by scientists in every field learning to judge their statistical methods by comparison with the Bayesian method; but also by the idea that science itself is a special case of… (More)

- Aloak Kapoor, Russell Greiner Supervisor, Dale Schuurmans, Peter Hooper
- 2005

The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatever without the author's prior written permission. The undersigned certify that they have… (More)

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