Gregory F. Cooper | DeepAI

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∙ 07/18/2022

The m-connecting imset and factorization for ADMG models

Directed acyclic graph (DAG) models have become widely studied and appli...

0 Bryan Andrews, et al. ∙

∙ 03/29/2020

Learning Latent Causal Structures with a Redundant Input Neural Network

Most causal discovery algorithms find causal structure among a set of ob...

9 Jonathan D. Young, et al. ∙

∙ 12/22/2017

Obtaining Accurate Probabilistic Causal Inference by Post-Processing Calibration

Discovery of an accurate causal Bayesian network structure from observat...

0 Fattaneh Jabbari, et al. ∙

∙ 11/16/2015

Binary Classifier Calibration using an Ensemble of Near Isotonic Regression Models

Learning accurate probabilistic models from data is crucial in many prac...

0 Mahdi Pakdaman Naeini, et al. ∙

∙ 07/09/2014

Counting Markov Blanket Structures

Learning Markov blanket (MB) structures has proven useful in performing ...

0 Shyam Visweswaran, et al. ∙

∙ 01/14/2014

Binary Classifier Calibration: Non-parametric approach

Accurate calibration of probabilistic predictive models learned is criti...

0 Mahdi Pakdaman Naeini, et al. ∙

∙ 01/13/2014

Binary Classifier Calibration: Bayesian Non-Parametric Approach

A set of probabilistic predictions is well calibrated if the events that...

0 Mahdi Pakdaman Naeini, et al. ∙

∙ 03/27/2013

An Algorithm for Computing Probabilistic Propositions

A method for computing probabilistic propositions is presented. It assum...

0 Gregory F. Cooper, et al. ∙

∙ 03/27/2013

Stochastic Simulation of Bayesian Belief Networks

This paper examines Bayesian belief network inference using simulation a...

0 Homer L. Chin, et al. ∙

∙ 03/27/2013

Updating Probabilities in Multiply-Connected Belief Networks

This paper focuses on probability updates in multiply-connected belief n...

0 Jaap Suermondt, et al. ∙

∙ 03/27/2013

A Method for Using Belief Networks as Influence Diagrams

This paper demonstrates a method for using belief-network algorithms to ...

0 Gregory F. Cooper, et al. ∙

∙ 03/27/2013

KNET: Integrating Hypermedia and Bayesian Modeling

KNET is a general-purpose shell for constructing expert systems based on...

0 R. Martin Chavez, et al. ∙

∙ 03/27/2013

Bounded Conditioning: Flexible Inference for Decisions under Scarce Resources

We introduce a graceful approach to probabilistic inference called bound...

0 Eric J. Horvitz, et al. ∙

∙ 03/27/2013

An Empirical Evaluation of a Randomized Algorithm for Probabilistic Inference

In recent years, researchers in decision analysis and artificial intelli...

0 R. Martin Chavez, et al. ∙

∙ 03/27/2013

An Empirical Analysis of Likelihood-Weighting Simulation on a Large, Multiply-Connected Belief Network

We analyzed the convergence properties of likelihood- weighting algorith...

0 Michael Shwe, et al. ∙

∙ 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. ∙

∙ 03/27/2013

A Randomized Approximation Algorithm of Logic Sampling

In recent years, researchers in decision analysis and artificial intelli...

0 R. Martin Chavez, et al. ∙

∙ 03/27/2013

Kutato: An Entropy-Driven System for Construction of Probabilistic Expert Systems from Databases

Kutato is a system that takes as input a database of cases and produces ...

0 Edward H. Herskovits, et al. ∙

∙ 03/20/2013

A Bayesian Method for Constructing Bayesian Belief Networks from Databases

This paper presents a Bayesian method for constructing Bayesian belief n...

0 Gregory F. Cooper, et al. ∙

∙ 02/27/2013

An Evaluation of an Algorithm for Inductive Learning of Bayesian Belief Networks Usin

Bayesian learning of belief networks (BLN) is a method for automatically...

0 Constantin F. Aliferis, et al. ∙

∙ 02/13/2013

A Structurally and Temporally Extended Bayesian Belief Network Model: Definitions, Properties, and Modeling Techniques

We developed the language of Modifiable Temporal Belief Networks (MTBNs)...

0 Constantin F. Aliferis, et al. ∙

∙ 01/30/2013

A Multivariate Discretization Method for Learning Bayesian Networks from Mixed Data

In this paper we address the problem of discretization in the context of...

0 Stefano Monti, et al. ∙

∙ 01/23/2013

A Bayesian Network Classifier that Combines a Finite Mixture Model and a Naive Bayes Model

In this paper we present a new Bayesian network model for classification...

0 Stefano Monti, et al. ∙

∙ 01/23/2013

Causal Discovery from a Mixture of Experimental and Observational Data

This paper describes a Bayesian method for combining an arbitrary mixtur...

0 Gregory F. Cooper, et al. ∙

∙ 01/16/2013

A Bayesian Method for Causal Modeling and Discovery Under Selection

This paper describes a Bayesian method for learning causal networks usin...

0 Gregory F. Cooper, et al. ∙

∙ 12/12/2012

A Bayesian Network Scoring Metric That Is Based On Globally Uniform Parameter Priors

We introduce a new Bayesian network (BN) scoring metric called the Globa...

0 Mehmet Kayaalp, et al. ∙

∙ 07/11/2012

Bayesian Biosurveillance of Disease Outbreaks

Early, reliable detection of disease outbreaks is a critical problem tod...

0 Gregory F. Cooper, et al. ∙

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