Search Results for author:
Found 51 papers, 1 papers with code
Heckerthoughts
This manuscript is technical memoir about my work at Stanford and Microsoft Research.
Likelihoods and Parameter Priors for Bayesian Networks
We develop simple methods for constructing likelihoods and parameter priors for learning about the parameters and structure of a Bayesian network.
Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributions
We develop simple methods for constructing parameter priors for model choice among Directed Acyclic Graphical (DAG) models.
A Tutorial on Learning With Bayesian Networks
A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest.
Probabilistic Similarity Networks
I then use these representations to build Pathfinder, a large normative expert system for the diagnosis of lymph-node diseases (the domain contains over 60 diseases and over 100 disease findings).
Embedded Bayesian Network Classifiers
Also, we show how to identify a non-redundant set of parameters for an EBNC, and describe an asymptotic approximation for learning the structure of Bayesian networks that contain EBNCs.
Accounting for hidden common causes when inferring cause and effect from observational data
Identifying causal relationships from observation data is difficult, in large part, due to the presence of hidden common causes.
Dependence and Relevance: A probabilistic view
We examine three probabilistic concepts related to the sentence "two variables have no bearing on each other".
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 measures or change in belief for reasoning with uncertainty in artificial intelligence research.
Addendum on the scoring of Gaussian directed acyclic graphical models
We provide a correction to the expression for scoring Gaussian directed acyclic graphical models derived in Geiger and Heckerman [Ann.
Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence (1993)
This is the Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence, which was held in Washington, DC, July 9-11, 1993
Problem Formulation as the Reduction of a Decision Model
In this paper, we extend the QMRDT probabilistic model for the domain of internal medicine to include decisions about treatments.
An Empirical Comparison of Three Inference Methods
In this paper, an empirical evaluation of three inference methods for uncertain reasoning is presented in the context of Pathfinder, a large expert system for the diagnosis of lymph-node pathology.
Similarity Networks for the Construction of Multiple-Faults Belief Networks
A similarity network is a tool for constructing belief networks for the diagnosis of a single fault.
A Perspective on Confidence and Its Use in Focusing Attention During Knowledge Acquisition
We present a representation of partial confidence in belief and preference that is consistent with the tenets of decision-theory.
A Tractable Inference Algorithm for Diagnosing Multiple Diseases
The time complexity of the algorithm is O(nm-2m+), where n is the number of diseases, m+ is the number of positive findings and m- is the number of negative findings.
An Axiomatic Framework for Belief Updates
In the spirit of Cox, properties for a measure of change in belief are enumerated.
A Backwards View for Assessment
Much artificial intelligence research focuses on the problem of deducing the validity of unobservable propositions or hypotheses from observable evidence.!
The Compilation of Decision Models
We introduce and analyze the problem of the compilation of decision models from a decision-theoretic perspective.
The Role of Calculi in Uncertain Inference Systems
Architectures for uncertainty handling that take statements in the calculus as objects to be reasoned about offer the prospect of retaining normative status with respect to decision making while supporting the other tasks in uncertain reasoning.
The Myth of Modularity in Rule-Based Systems
However, we argue that in the case of plausible reasoning, rules are syntactically modular but are rarely semantically modular.
Probabilistic Interpretations for MYCIN's Certainty Factors
This inconsistency is used to argue for a redefinition of certainty factors in terms of the intuitively appealing desiderata associated with the combining functions.
A Combination of Cutset Conditioning with Clique-Tree Propagation in the Pathfinder System
Cutset conditioning and clique-tree propagation are two popular methods for performing exact probabilistic inference in Bayesian belief networks.
Separable and transitive graphoids
We examine three probabilistic formulations of the sentence a and b are totally unrelated with respect to a given set of variables U.
Advances in Probabilistic Reasoning
This paper discuses multiple Bayesian networks representation paradigms for encoding asymmetric independence assertions.
An Approximate Nonmyopic Computation for Value of Information
Value-of-information analyses provide a straightforward means for selecting the best next observation to make, and for determining whether it is better to gather additional information or to act immediately.
Causal Independence for Knowledge Acquisition and Inference
I introduce a temporal belief-network representation of causal independence that a knowledge engineer can use to elicit probabilistic models.
Diagnosis of Multiple Faults: A Sensitivity Analysis
We compare the diagnostic accuracy of three diagnostic inference models: the simple Bayes model, the multimembership Bayes model, which is isomorphic to the parallel combination function in the certainty-factor model, and a model that incorporates the noisy OR-gate interaction.
Inference Algorithms for Similarity Networks
We examine two types of similarity networks each based on a distinct notion of relevance.
Learning Gaussian Networks
We describe algorithms for learning Bayesian networks from a combination of user knowledge and statistical data.
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
Second, we describe local search and annealing algorithms to be used in conjunction with scoring metrics.
A New Look at Causal Independence
In this representation, the interaction between causes and effect can be written as a nested decomposition of functions.
A Decision-Based View of Causality
Using this definition, we show how causal dependence can be represented within an influence diagram.
A Bayesian Approach to Learning Causal Networks
We show that these new assumptions, when combined with parameter independence, parameter modularity, and likelihood equivalence, allow us to apply methods for learning acausal networks to learn causal networks.
A Definition and Graphical Representation for Causality
We present a precise definition of cause and effect in terms of a fundamental notion called unresponsiveness.
Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains
We examine Bayesian methods for learning Bayesian networks from a combination of prior knowledge and statistical data.
Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment
We develop and extend existing decision-theoretic methods for troubleshooting a nonfunctioning device.
Asymptotic Model Selection for Directed Networks with Hidden Variables
We extend the Bayesian Information Criterion (BIC), an asymptotic approximation for the marginal likelihood, to Bayesian networks with hidden variables.
Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network
We consider the Laplace approximation and the less accurate but more efficient BIC/MDL approximation.
A Bayesian Approach to Learning Bayesian Networks with Local Structure
The majority of this work has concentrated on using decision-tree representations for the CPDs.
Structure and Parameter Learning for Causal Independence and Causal Interaction Models
This paper discusses causal independence models and a generalization of these models called causal interaction models.
Learning Mixtures of DAG Models
We describe computationally efficient methods for learning mixtures in which each component is a directed acyclic graphical model (mixtures of DAGs or MDAGs).
An Experimental Comparison of Several Clustering and Initialization Methods
In the first part of the paper, we perform an experimental comparison between three batch clustering algorithms: the Expectation-Maximization (EM) algorithm, a winner take all version of the EM algorithm reminiscent of the K-means algorithm, and model-based hierarchical agglomerative clustering.
Empirical Analysis of Predictive Algorithms for Collaborative Filtering
Results indicate that for a wide range of conditions, Bayesian networks with decision trees at each node and correlation methods outperform Bayesian-clustering and vector-similarity methods.
Inferring Informational Goals from Free-Text Queries: A Bayesian Approach
People using consumer software applications typically do not use technical jargon when querying an online database of help topics.
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 that satisfies global parameter independence, complete model equivalence, and some weak regularity assumptions, is the normal-Wishart distribution.
Fast Learning from Sparse Data
We describe two techniques that significantly improve the running time of several standard machine-learning algorithms when data is sparse.
An MDP-based Recommender System
We argue that it is more appropriate to view the problem of generating recommendations as a sequential decision problem and, consequently, that Markov decision processes (MDP) provide a more appropriate model for Recommender systems.
CFW: A Collaborative Filtering System Using Posteriors Over Weights Of Evidence
We describe CFW, a computationally efficient algorithm for collaborative filtering that uses posteriors over weights of evidence.
Continuous Time Dynamic Topic Models
In contrast to the cDTM, the original discrete-time dynamic topic model (dDTM) requires that time be discretized.
A powerful and efficient set test for genetic markers that handles confounders
Until now, these approaches did not address confounding by family relatedness and population structure, a problem that is becoming more important as larger data sets are used to increase power.
