
A Qualitative Markov Assumption and its Implications for Belief Change
The study of belief change has been an active area in philosophy and AI....
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ContextSpecific Independence in Bayesian Networks
Bayesian networks provide a language for qualitatively representing the ...
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Image Segmentation in Video Sequences: A Probabilistic Approach
"Background subtraction" is an old technique for finding moving objects ...
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Sequential Update of Bayesian Network Structure
There is an obvious need for improving the performance and accuracy of a...
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Learning the Structure of Dynamic Probabilistic Networks
Dynamic probabilistic networks are a compact representation of complex s...
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ModelBased Bayesian Exploration
Reinforcement learning systems are often concerned with balancing explor...
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Discovering the Hidden Structure of Complex Dynamic Systems
Dynamic Bayesian networks provide a compact and natural representation f...
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Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (2002)
This is the Proceedings of the Eighteenth Conference on Uncertainty in A...
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Likelihood Computations Using Value Abstractions
In this paper, we use evidencespecific value abstraction for speeding B...
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Incorporating Expressive Graphical Models in Variational Approximations: ChainGraphs and Hidden Variables
Global variational approximation methods in graphical models allow effic...
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Gibbs Sampling in Factorized ContinuousTime Markov Processes
A central task in many applications is reasoning about processes that ch...
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Convexifying the Bethe Free Energy
The introduction of loopy belief propagation (LBP) revitalized the appli...
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Mean Field Variational Approximation for ContinuousTime Bayesian Networks
Continuoustime Bayesian networks is a natural structured representation...
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On the Sample Complexity of Learning Bayesian Networks
In recent years there has been an increasing interest in learning Bayesi...
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Learning Bayesian Networks with Local Structure
In this paper we examine a novel addition to the known methods for learn...
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The Bayesian Structural EM Algorithm
In recent years there has been a flurry of works on learning Bayesian ne...
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Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm
Learning Bayesian networks is often cast as an optimization problem, whe...
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Data Analysis with Bayesian Networks: A Bootstrap Approach
In recent years there has been significant progress in algorithms and me...
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Gaussian Process Networks
In this paper we address the problem of learning the structure of a Baye...
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Being Bayesian about Network Structure
In many domains, we are interested in analyzing the structure of the und...
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Multivariate Information Bottleneck
The Information bottleneck method is an unsupervised nonparametric data...
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Learning the Dimensionality of Hidden Variables
A serious problem in learning probabilistic models is the presence of hi...
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Learning Module Networks
Methods for learning Bayesian network structure can discover dependency ...
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The Information Bottleneck EM Algorithm
Learning with hidden variables is a central challenge in probabilistic g...
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"Ideal Parent" Structure Learning for Continuous Variable Networks
In recent years, there is a growing interest in learning Bayesian networ...
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Plausibility Measures and Default Reasoning
We introduce a new approach to modeling uncertainty based on plausibilit...
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FirstOrder Conditional Logic Revisited
Conditional logics play an important role in recent attempts to formulat...
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Nir Friedman
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Professor, The Rachel and Selim Benin School of Computer Science and Engineering, The Alexander Silberman Institute of Life Sciences at The Hebrew University of Jerusalem