A. Pfeffer. Probabilistic Reasoning for Complex Systems. PhD thesis, Stanford, 2000. Home/Search Document Not in Database Summary Related Articles Check |
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Identity Uncertainty and Citation Matching - Pasula, Marthi, Milch, Russell, .. (2003) (16 citations) (Correct)
....systems have used domain specific algorithms and data structures; the probabilistic approaches are based on a fixed probability model. In previous work [10] we have suggested a declarative approach to identity uncertainty using a formal language an extension of relational probability models [11]. Here, we describe the first substantial application of the approach. Section 2 explains how to specify a generative probability model of the domain. The key technical point (Section 3) is that the possible worlds include not only objects and relations but also mappings from terms in the language ....
....by our system point to some interesting unmodeled aspects of the citation process. 2 RPMs Reasoning about identity requires reasoning about objects, which requires at least some of the expressive power of a first order logical language. Our approach builds on relational probability models (RPMs) [11], which let us specify probability models over possible worlds defined by objects, properties, classes, and relations. 2.1 Basic RPMs At its most basic, relational probability model, as defined by Koller et al. [12] consists of A set C of classes denoting sets of objects, related by ....
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A. Pfeffer. Probabilistic Reasoning for Complex Systems. PhD thesis, Stanford, 2000.
Representational Power of Probabilistic-Logical Models: From.. - Kersting (Correct)
....language concepts makes a general understanding of PLMs and learning PLMs difficult if not impossible. # This is a position statement for the IJCAI 2003 Workshop on Learning Statistical Models from Relational Data Avi Pfeffers s interesting PhD thesis provides some more references, [Pfeffer, 2000] . 3 Downgrading Downgrading consists of two steps. Step 1) Choose a generally applicable (learning) PLM. The PLM should cover the basic language concepts proposed in the different scientific subareas: Relations among random variables or states to model uncertainty. This subsumes ....
A. J Pfeffer. Probabilistic Reasoning for Complex Systems. PhD thesis, Stanford University, 2000.
Parameter Learning in Object Oriented Bayesian Networks - Langseth, Bangsø (2001) (1 citation) (Correct)
....So far we have assumed that the domain expert is able to unambiguously classify each instantiation of the domain to a specific class. However, this may not be realistic in real world applications. Not being able to classify an instantiation is an example of what is called type uncertainty in [33]: The expert is uncertain about the type (or class in our terminology) of an instantiation. As an example, assume OMD is unable to determine whether COWlisaMilk cow or a Meat cow. Even though he is not able to determine the class of COWl, he would like to learn from the available data. This ....
A.J. Pfeffer, Probabilistic reasoning for complex systems, Ph.D. thesis, Stanford University (2000).
Semantics and Inference for Recursive Probability Models - Pfeffer, Koller (2000) (3 citations) Self-citation (Pfeffer) (Correct)
....has more than one model. Interestingly, however, it turns out that every KB has at least one model. Theorem 3.7: Let K be a recursive probabilistic KB. There exists a probability measure over( Omega K ; EK ) that is a model for K. Sketch of proof (full proofs of all theorems can be found in (Pfeffer 2000)) We begin by defining a sequence of larger and larger self contained sets of variables, that eventually covers all the variables of K. We can achieve this by setting X i to be the set of variables whose chains have length i. Such sets are self contained for our language. Next, for i j, we ....
Pfeffer, A. 2000. Probabilistic Reasoning for Complex Systems. Ph.D. Dissertation, Stanford University.
Identity Uncertainty and Citation Matching - Pasula, Marthi, Milch, Russell, .. (2002) (16 citations) (Correct)
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A. Pfeffer. Probabilistic Reasoning for Complex Systems. PhD thesis, Stanford, 2000.
First-Order Probabilistic Models for Information Extraction - Marthi, Milch, Russell (2003) (1 citation) (Correct)
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A. Pfeffer. Probabilistic Reasoning for Complex Systems. PhD thesis, Stanford, 2000.
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