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UAI
1993
10 years 5 months ago
Inference Algorithms for Similarity Networks
We examine two types of similarity networks each based on a distinct notion of relevance. For both types of similarity networks we present an efficient inference algorithm that wo...
Dan Geiger, David Heckerman
UAI
1997
10 years 5 months ago
Object-Oriented Bayesian Networks
Bayesian networks provide a modeling language and associated inference algorithm for stochastic domains. They have been successfully applied in a variety of medium-scale applicati...
Daphne Koller, Avi Pfeffer
NIPS
2000
10 years 5 months ago
Learning Switching Linear Models of Human Motion
The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. Effective models of human dynamics can be learned from motion capture data usi...
Vladimir Pavlovic, James M. Rehg, John MacCormick
NIPS
1998
10 years 5 months ago
Approximate Learning of Dynamic Models
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
Xavier Boyen, Daphne Koller
AAAI
1997
10 years 5 months ago
Effective Bayesian Inference for Stochastic Programs
In this paper, we propose a stochastic version of a general purpose functional programming language as a method of modeling stochastic processes. The language contains random choi...
Daphne Koller, David A. McAllester, Avi Pfeffer
ASC
2000
10 years 5 months ago
A New Object-Oriented Stochastic Modeling Language
A new language and inference algorithm for stochastic modeling is presented. This work refines and generalizes the stochastic functional language originally proposed by [1]. The l...
Daniel Pless, George F. Luger, Carl R. Stern
UAI
2004
10 years 5 months ago
Graph Partition Strategies for Generalized Mean Field Inference
An autonomous variational inference algorithm for arbitrary graphical models requires the ability to optimize variational approximations over the space of model parameters as well...
Eric P. Xing, Michael I. Jordan
IJCAI
2003
10 years 5 months ago
First-order probabilistic inference
Most probabilistic inference algorithms are specified and processed on a propositional level. In the last decade, many proposals for algorithms accepting first-order specificat...
David Poole
NIPS
2001
10 years 5 months ago
Linear-time inference in Hierarchical HMMs
The hierarchical hidden Markov model (HHMM) is a generalization of the hidden Markov model (HMM) that models sequences with structure at many length/time scales [FST98]. Unfortuna...
K. P. Murphy, Mark A. Paskin
DLOG
2006
10 years 5 months ago
Description logic reasoning using the PTTP approach
The goal of this paper is to present how the Prolog Technology Theorem Proving (PTTP) approach can be used for ABox-reasoning. This work presents an inference algorithm over the l...
Zsolt Nagy, Gergely Lukácsy, Péter S...
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