David Heckerman

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∙ 02/13/2023

Heckerthoughts

This manuscript is technical memoir about my work at Stanford and Micros...

0 David Heckerman, et al. ∙

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∙ 05/13/2021

Likelihoods and Parameter Priors for Bayesian Networks

We develop simple methods for constructing likelihoods and parameter pri...

0 David Heckerman, et al. ∙

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∙ 02/01/2020

A Tutorial on Learning With Bayesian Networks

A Bayesian network is a graphical model that encodes probabilistic relat...

0 David Heckerman, et al. ∙

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∙ 11/06/2019

Probabilistic Similarity Networks

Normative expert systems have not become commonplace because they have b...

0 David Heckerman, et al. ∙

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∙ 10/22/2019

Embedded Bayesian Network Classifiers

Low-dimensional probability models for local distribution functions in a...

0 David Heckerman, et al. ∙

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∙ 01/02/2018

Accounting for hidden common causes when inferring cause and effect from observational data

Identifying causal relationships from observation data is difficult, in ...

0 David Heckerman, et al. ∙

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∙ 01/02/2018

Accounting for hidden common causes when infering cause and effect from observational data

Identifying causal relationships from observation data is difficult, in ...

0 David Heckerman, et al. ∙

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∙ 10/27/2016

Dependence and Relevance: A probabilistic view

We examine three probabilistic concepts related to the sentence "two var...

0 Dan Geiger, et al. ∙

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∙ 07/27/2014

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...

0 Eric J. Horvitz, et al. ∙

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∙ 02/27/2014

Addendum on the scoring of Gaussian directed acyclic graphical models

We provide a correction to the expression for scoring Gaussian directed ...

0 Jack Kuipers, et al. ∙

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∙ 04/13/2013

Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence (1993)

This is the Proceedings of the Ninth Conference on Uncertainty in Artifi...

0 David Heckerman, et al. ∙

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∙ 03/27/2013

Probabilistic Interpretations for MYCIN's Certainty Factors

This paper examines the quantities used by MYCIN to reason with uncertai...

0 David Heckerman, et al. ∙

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∙ 03/27/2013

A Backwards View for Assessment

Much artificial intelligence research focuses on the problem of deducing...

0 Ross D. Shachter, et al. ∙

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∙ 03/27/2013

An Axiomatic Framework for Belief Updates

In the 1940's, a physicist named Cox provided the first formal justifica...

0 David Heckerman, et al. ∙

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∙ 03/27/2013

The Myth of Modularity in Rule-Based Systems

In this paper, we examine the concept of modularity, an often cited adva...

0 David Heckerman, et al. ∙

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∙ 03/27/2013

The Role of Calculi in Uncertain Inference Systems

Much of the controversy about methods for automated decision making has ...

0 Michael P. Wellman, et al. ∙

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∙ 03/27/2013

A Perspective on Confidence and Its Use in Focusing Attention During Knowledge Acquisition

We present a representation of partial confidence in belief and preferen...

0 David Heckerman, et al. ∙

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∙ 03/27/2013

An Empirical Comparison of Three Inference Methods

In this paper, an empirical evaluation of three inference methods for un...

0 David Heckerman, et al. ∙

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∙ 03/27/2013

A Tractable Inference Algorithm for Diagnosing Multiple Diseases

We examine a probabilistic model for the diagnosis of multiple diseases....

0 David Heckerman, et al. ∙

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∙ 03/27/2013

The Compilation of Decision Models

We introduce and analyze the problem of the compilation of decision mode...

0 David Heckerman, et al. ∙

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∙ 03/27/2013

Separable and transitive graphoids

We examine three probabilistic formulations of the sentence a and b are ...

0 Dan Geiger, et al. ∙

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∙ 03/27/2013

A Combination of Cutset Conditioning with Clique-Tree Propagation in the Pathfinder System

Cutset conditioning and clique-tree propagation are two popular methods ...

0 Jaap Suermondt, et al. ∙

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∙ 03/27/2013

Problem Formulation as the Reduction of a Decision Model

In this paper, we extend the QMRDT probabilistic model for the domain of...

0 David Heckerman, et al. ∙

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∙ 03/27/2013

Similarity Networks for the Construction of Multiple-Faults Belief Networks

A similarity network is a tool for constructing belief networks for the ...

0 David Heckerman, et al. ∙

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∙ 03/20/2013

An Approximate Nonmyopic Computation for Value of Information

Value-of-information analyses provide a straightforward means for select...

0 David Heckerman, et al. ∙

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∙ 03/20/2013

Advances in Probabilistic Reasoning

This paper discuses multiple Bayesian networks representation paradigms ...

0 Dan Geiger, et al. ∙

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∙ 03/06/2013

Inference Algorithms for Similarity Networks

We examine two types of similarity networks each based on a distinct not...

0 Dan Geiger, et al. ∙

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∙ 03/06/2013

Causal Independence for Knowledge Acquisition and Inference

I introduce a temporal belief-network representation of causal independe...

0 David Heckerman, et al. ∙

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∙ 03/06/2013

Diagnosis of Multiple Faults: A Sensitivity Analysis

We compare the diagnostic accuracy of three diagnostic inference models:...

0 David Heckerman, et al. ∙

research

∙ 02/27/2013

A Decision-Based View of Causality

Most traditional models of uncertainty have focused on the associational...

0 David Heckerman, et al. ∙

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∙ 02/27/2013

Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

We describe algorithms for learning Bayesian networks from a combination...

0 David Heckerman, et al. ∙

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∙ 02/27/2013

A New Look at Causal Independence

Heckerman (1993) defined causal independence in terms of a set of tempor...

0 David Heckerman, et al. ∙

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∙ 02/27/2013

Learning Gaussian Networks

We describe algorithms for learning Bayesian networks from a combination...

0 Dan Geiger, et al. ∙

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∙ 02/13/2013

Asymptotic Model Selection for Directed Networks with Hidden Variables

We extend the Bayesian Information Criterion (BIC), an asymptotic approx...

0 Dan Geiger, et al. ∙

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∙ 02/13/2013

Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network

We discuss Bayesian methods for learning Bayesian networks when data set...

0 David Maxwell Chickering, et al. ∙

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∙ 02/13/2013

Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment

We develop and extend existing decision-theoretic methods for troublesho...

0 John S. Breese, et al. ∙

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∙ 02/06/2013

Structure and Parameter Learning for Causal Independence and Causal Interaction Models

This paper discusses causal independence models and a generalization of ...

0 Christopher Meek, et al. ∙

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∙ 02/06/2013

Models and Selection Criteria for Regression and Classification

When performing regression or classification, we are interested in the c...

0 David Heckerman, et al. ∙

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∙ 02/06/2013

A Bayesian Approach to Learning Bayesian Networks with Local Structure

Recently several researchers have investigated techniques for using data...

0 David Maxwell Chickering, et al. ∙

research

∙ 01/30/2013

Learning Mixtures of DAG Models

We describe computationally efficient methods for learning mixtures in w...

0 Bo Thiesson, et al. ∙

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∙ 01/30/2013

An Experimental Comparison of Several Clustering and Initialization Methods

We examine methods for clustering in high dimensions. In the first part ...

0 Marina Meila, et al. ∙

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∙ 01/30/2013

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...

0 Eric J. Horvitz, et al. ∙

research

∙ 01/30/2013

Inferring Informational Goals from Free-Text Queries: A Bayesian Approach

People using consumer software applications typically do not use technic...

0 David Heckerman, et al. ∙

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∙ 01/23/2013

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...

0 Dan Geiger, et al. ∙

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∙ 01/23/2013

Fast Learning from Sparse Data

We describe two techniques that significantly improve the running time o...

0 David Maxwell Chickering, et al. ∙

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∙ 01/16/2013

Dependency Networks for Collaborative Filtering and Data Visualization

We describe a graphical model for probabilistic relationships---an alter...

0 David Heckerman, et al. ∙

research

∙ 01/16/2013

A Decision Theoretic Approach to Targeted Advertising

A simple advertising strategy that can be used to help increase sales of...

0 David Maxwell Chickering, et al. ∙

research

∙ 12/12/2012

An MDP-based Recommender System

Typical Recommender systems adopt a static view of the recommendation pr...

0 Guy Shani, et al. ∙

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∙ 12/12/2012

Staged Mixture Modelling and Boosting

In this paper, we introduce and evaluate a data-driven staged mixture mo...

0 Christopher Meek, et al. ∙

research

∙ 12/12/2012

CFW: A Collaborative Filtering System Using Posteriors Over Weights Of Evidence

We describe CFW, a computationally efficient algorithm for collaborative...

0 Carl Kadie, et al. ∙

David Heckerman

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