research
∙
07/18/2022
The m-connecting imset and factorization for ADMG models
Directed acyclic graph (DAG) models have become widely studied and appli...
share
research
∙
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...
research
∙
12/22/2017
Obtaining Accurate Probabilistic Causal Inference by Post-Processing Calibration
Discovery of an accurate causal Bayesian network structure from observat...
research
∙
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...
research
∙
07/09/2014
Counting Markov Blanket Structures
Learning Markov blanket (MB) structures has proven useful in performing ...
research
∙
01/14/2014
Binary Classifier Calibration: Non-parametric approach
Accurate calibration of probabilistic predictive models learned is criti...
research
∙
01/13/2014
Binary Classifier Calibration: Bayesian Non-Parametric Approach
A set of probabilistic predictions is well calibrated if the events that...
research
∙
03/27/2013
An Algorithm for Computing Probabilistic Propositions
A method for computing probabilistic propositions is presented. It assum...
research
∙
03/27/2013
Stochastic Simulation of Bayesian Belief Networks
This paper examines Bayesian belief network inference using simulation a...
research
∙
03/27/2013
Updating Probabilities in Multiply-Connected Belief Networks
This paper focuses on probability updates in multiply-connected belief n...
research
∙
03/27/2013
A Method for Using Belief Networks as Influence Diagrams
This paper demonstrates a method for using belief-network algorithms to ...
research
∙
03/27/2013
KNET: Integrating Hypermedia and Bayesian Modeling
KNET is a general-purpose shell for constructing expert systems based on...
research
∙
03/27/2013
Bounded Conditioning: Flexible Inference for Decisions under Scarce Resources
We introduce a graceful approach to probabilistic inference called bound...
research
∙
03/27/2013
An Empirical Evaluation of a Randomized Algorithm for Probabilistic Inference
In recent years, researchers in decision analysis and artificial intelli...
research
∙
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...
research
∙
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 ...
research
∙
03/27/2013
A Randomized Approximation Algorithm of Logic Sampling
In recent years, researchers in decision analysis and artificial intelli...
research
∙
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 ...
research
∙
03/20/2013
A Bayesian Method for Constructing Bayesian Belief Networks from Databases
This paper presents a Bayesian method for constructing Bayesian belief n...
research
∙
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...
research
∙
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)...
research
∙
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...
research
∙
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...
research
∙
01/23/2013
Causal Discovery from a Mixture of Experimental and Observational Data
This paper describes a Bayesian method for combining an arbitrary mixtur...
research
∙
01/16/2013
A Bayesian Method for Causal Modeling and Discovery Under Selection
This paper describes a Bayesian method for learning causal networks usin...
research
∙
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...
research
∙
07/11/2012