
Likelihoods and Parameter Priors for Bayesian Networks
We develop simple methods for constructing likelihoods and parameter pri...
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A Tutorial on Learning With Bayesian Networks
A Bayesian network is a graphical model that encodes probabilistic relat...
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Probabilistic Similarity Networks
Normative expert systems have not become commonplace because they have b...
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Embedded Bayesian Network Classifiers
Lowdimensional probability models for local distribution functions in a...
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Accounting for hidden common causes when inferring cause and effect from observational data
Identifying causal relationships from observation data is difficult, in ...
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Accounting for hidden common causes when infering cause and effect from observational data
Identifying causal relationships from observation data is difficult, in ...
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Dependence and Relevance: A probabilistic view
We examine three probabilistic concepts related to the sentence "two var...
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Modular Belief Updates and Confusion about Measures of Certainty in Artificial Intelligence Research
Over the last decade, there has been growing interest in the use or meas...
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Addendum on the scoring of Gaussian directed acyclic graphical models
We provide a correction to the expression for scoring Gaussian directed ...
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Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence (1993)
This is the Proceedings of the Ninth Conference on Uncertainty in Artifi...
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Probabilistic Interpretations for MYCIN's Certainty Factors
This paper examines the quantities used by MYCIN to reason with uncertai...
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A Backwards View for Assessment
Much artificial intelligence research focuses on the problem of deducing...
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An Axiomatic Framework for Belief Updates
In the 1940's, a physicist named Cox provided the first formal justifica...
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The Myth of Modularity in RuleBased Systems
In this paper, we examine the concept of modularity, an often cited adva...
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The Role of Calculi in Uncertain Inference Systems
Much of the controversy about methods for automated decision making has ...
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A Perspective on Confidence and Its Use in Focusing Attention During Knowledge Acquisition
We present a representation of partial confidence in belief and preferen...
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An Empirical Comparison of Three Inference Methods
In this paper, an empirical evaluation of three inference methods for un...
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A Tractable Inference Algorithm for Diagnosing Multiple Diseases
We examine a probabilistic model for the diagnosis of multiple diseases....
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The Compilation of Decision Models
We introduce and analyze the problem of the compilation of decision mode...
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Separable and transitive graphoids
We examine three probabilistic formulations of the sentence a and b are ...
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A Combination of Cutset Conditioning with CliqueTree Propagation in the Pathfinder System
Cutset conditioning and cliquetree propagation are two popular methods ...
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Problem Formulation as the Reduction of a Decision Model
In this paper, we extend the QMRDT probabilistic model for the domain of...
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Similarity Networks for the Construction of MultipleFaults Belief Networks
A similarity network is a tool for constructing belief networks for the ...
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An Approximate Nonmyopic Computation for Value of Information
Valueofinformation analyses provide a straightforward means for select...
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Advances in Probabilistic Reasoning
This paper discuses multiple Bayesian networks representation paradigms ...
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Inference Algorithms for Similarity Networks
We examine two types of similarity networks each based on a distinct not...
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Causal Independence for Knowledge Acquisition and Inference
I introduce a temporal beliefnetwork representation of causal independe...
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Diagnosis of Multiple Faults: A Sensitivity Analysis
We compare the diagnostic accuracy of three diagnostic inference models:...
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A DecisionBased View of Causality
Most traditional models of uncertainty have focused on the associational...
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Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
We describe algorithms for learning Bayesian networks from a combination...
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A New Look at Causal Independence
Heckerman (1993) defined causal independence in terms of a set of tempor...
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Learning Gaussian Networks
We describe algorithms for learning Bayesian networks from a combination...
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Asymptotic Model Selection for Directed Networks with Hidden Variables
We extend the Bayesian Information Criterion (BIC), an asymptotic approx...
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Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network
We discuss Bayesian methods for learning Bayesian networks when data set...
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DecisionTheoretic Troubleshooting: A Framework for Repair and Experiment
We develop and extend existing decisiontheoretic methods for troublesho...
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Structure and Parameter Learning for Causal Independence and Causal Interaction Models
This paper discusses causal independence models and a generalization of ...
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Models and Selection Criteria for Regression and Classification
When performing regression or classification, we are interested in the c...
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A Bayesian Approach to Learning Bayesian Networks with Local Structure
Recently several researchers have investigated techniques for using data...
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Learning Mixtures of DAG Models
We describe computationally efficient methods for learning mixtures in w...
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An Experimental Comparison of Several Clustering and Initialization Methods
We examine methods for clustering in high dimensions. In the first part ...
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The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users
The Lumiere Project centers on harnessing probability and utility to pro...
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Inferring Informational Goals from FreeText Queries: A Bayesian Approach
People using consumer software applications typically do not use technic...
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Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributions
We show that the only parameter prior for complete Gaussian DAG models t...
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Fast Learning from Sparse Data
We describe two techniques that significantly improve the running time o...
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Dependency Networks for Collaborative Filtering and Data Visualization
We describe a graphical model for probabilistic relationshipsan alter...
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A Decision Theoretic Approach to Targeted Advertising
A simple advertising strategy that can be used to help increase sales of...
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An MDPbased Recommender System
Typical Recommender systems adopt a static view of the recommendation pr...
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Staged Mixture Modelling and Boosting
In this paper, we introduce and evaluate a datadriven staged mixture mo...
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CFW: A Collaborative Filtering System Using Posteriors Over Weights Of Evidence
We describe CFW, a computationally efficient algorithm for collaborative...
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LargeSample Learning of Bayesian Networks is NPHard
In this paper, we provide new complexity results for algorithms that lea...
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David Heckerman
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Distinguished Scientist at Amazon and Chief Data Scientist at Human Longevity, Inc.