## Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains (2003)

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### BibTeX

@MISC{Langseth03fusionof,

author = {Helge Langseth and Thomas D. Nielsen and Richard Dybowski},

title = {Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains},

year = {2003}

}

### Years of Citing Articles

### OpenURL

### Abstract

When constructing a Bayesian network, it can be advantageous to employ structural learning algorithms to combine knowledge captured in databases with prior information provided by domain experts. Unfortunately, conventional learning algorithms do not easily incorporate prior information, if this information is too vague to be encoded as properties that are local to families of variables. For instance, conventional algorithms do not exploit prior information about repetitive structures, which are often found in object oriented domains such as computer networks, large pedigrees and genetic analysis.

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Citation Context ...alculating the quality of the approximation (see Cover and Thomas, 1991). One of them is the fact that the KL divergence bound the maximum error in the assessed probability for a particular event A, (=-=Whittaker, 1990-=-, Proposition 4.3.7): sup A # # # # # x#A f (x | Q)- x#Asf N (x|sF N ) # # # # # # # 1 2sD # f ||sf N # . Similar result for the maximal error of the estimated conditional distribution is derived by v... |

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Citation Context ...dman (1998) proves that by increasing 7. An active research area within the learning community is the discovery of hidden variables. These types of variables are never observed (Spirtes et al., 1993; =-=Friedman et al., 1998-=-; Elidan et al., 2000; Elidan and Friedman, 2001), however, hidden variables will not be considered further in this paper. 349 LANGSETH AND NIELSEN the expected score at each iteration we always obtai... |

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Citation Context ...g the integral in Equation 1. Thus, in order to apply these approximations we need to find the MAP parameters, for example by using the expectation-maximization (EM) algorithm (Dempster et al., 1977; =-=Green, 1990-=-), before we can calculate the score of a model. Thus, for each candidate model we may need to invest a considerable amount of time in order to evaluate the model. As an alternative, Friedman (1998) d... |

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Citation Context ...all influence upon the selected model, structural priors are most often used to encode ignorance, and in some cases to restrict model complexity. Examples include the uniform prior r(X i , P i ) = 1 (=-=Cooper and Herskovits, 1991-=-), and r(X i , P i ) = # n-1 | P i | # -1 351 LANGSETH AND NIELSEN used by Friedman and Koller (2003). Another prior which is frequently used is r(X i , P i ) = k d i (Heckerman et al., 1995), where 0... |

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Citation Context ...te that this approach can be seen as a generalization of the method for parameter learning in DBNs, see West and Harrison (1997). 17sLangseth and Nielsen according to a predefined query distribution (=-=Greiner et al., 1997-=-), the learning method would have been slightly different (the general approach, however, would still apply). The proposed method is tightly connected to the SEM-algorithm, described in Section 3.3. T... |

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Citation Context ...approach is to apply asymptotic approximations such as the Laplace approximation, (see, for example, Ripley, 1996), the Bayesian Information Criterion (Schwarz, 1978), the Minimum Description Length (=-=Rissanen, 1987-=-) or the CheesemanStutz approximation (Cheeseman and Stutz, 1996), see also Chichering and Heckerman (1997) for a discussion. These approximations assume that the posterior over the parameters is peak... |

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Citation Context ... by increasing 7. An active research area within the learning community is the discovery of hidden variables. These types of variables are never observed (Spirtes et al., 1993; Friedman et al., 1998; =-=Elidan et al., 2000-=-; Elidan and Friedman, 2001), however, hidden variables will not be considered further in this paper. 349 LANGSETH AND NIELSEN the expected score at each iteration we always obtain a better network in... |

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Citation Context ...active research area within the learning community is the discovery of hidden variables. These types of variables are never observed (Spirtes et al., 1993; Friedman et al., 1998; Elidan et al., 2000; =-=Elidan and Friedman, 2001-=-), however, hidden variables will not be considered further in this paper. 349 LANGSETH AND NIELSEN the expected score at each iteration we always obtain a better network in terms of its marginal scor... |

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