176 citations · 559 across the 17 of their papers we have counts for
17 papers
Qualitative Propagation and Scenario-based Explanation of Probabilistic Reasoning
Max Henrion, Marek J. Druzdzel
Comprehensible explanations of probabilistic reasoning are a prerequisite for wider acceptance of Bayesian methods in expert systems and decision support systems. A study of human…
Intercausal Reasoning with Uninstantiated Ancestor Nodes
Marek J. Druzdzel, Max Henrion
Intercausal reasoning is a common inference pattern involving probabilistic dependence of causes of an observed common effect. The sign of this dependence is captured by a qualitat…
Causality in Bayesian Belief Networks
Marek J. Druzdzel, Herbert A. Simon
We address the problem of causal interpretation of the graphical structure of Bayesian belief networks (BBNs). We review the concept of causality explicated in the domain of struct…
Some Properties of Joint Probability Distributions
Marek J. Druzdzel
Several Artificial Intelligence schemes for reasoning under uncertainty explore either explicitly or implicitly asymmetries among probabilities of various states of their uncertain…
Elicitation of Probabilities for Belief Networks: Combining Qualitative and Quantitative Information
Marek J. Druzdzel, Linda C. van der Gaag
Although the usefulness of belief networks for reasoning under uncertainty is widely accepted, obtaining numerical probabilities that they require is still perceived a major obstac…
Computational Advantages of Relevance Reasoning in Bayesian Belief Networks
Yan Lin, Marek J. Druzdzel
This paper introduces a computational framework for reasoning in Bayesian belief networks that derives significant advantages from focused inference and relevance reasoning. This f…
A Hybrid Anytime Algorithm for the Constructiion of Causal Models From Sparse Data
Denver Dash, Marek J. Druzdzel
We present a hybrid constraint-based/Bayesian algorithm for learning causal networks in the presence of sparse data. The algorithm searches the space of equivalence classes of mode…
User Interface Tools for Navigation in Conditional Probability Tables and Elicitation of Probabilities in Bayesian Networks
Haiqin Wang, Marek J. Druzdzel
Elicitation of probabilities is one of the most laborious tasks in building decision-theoretic models, and one that has so far received only moderate attention in decision-theoreti…
Causal Mechanism-based Model Construction
Tsai-Ching Lu, Marek J. Druzdzel, Tze-Yun Leong
We propose a framework for building graphical causal model that is based on the concept of causal mechanisms. Causal models are intuitive for human users and, more importantly, sup…
Computational Investigation of Low-Discrepancy Sequences in Simulation Algorithms for Bayesian Networks
Jian Cheng, Marek J. Druzdzel
Monte Carlo sampling has become a major vehicle for approximate inference in Bayesian networks. In this paper, we investigate a family of related simulation approaches, known colle…
Confidence Inference in Bayesian Networks
Jian Cheng, Marek J. Druzdzel
We present two sampling algorithms for probabilistic confidence inference in Bayesian networks. These two algorithms (we call them AIS-BN-mu and AIS-BN-sigma algorithms) guarantee…
An Importance Sampling Algorithm Based on Evidence Pre-propagation
Changhe Yuan, Marek J. Druzdzel
Precision achieved by stochastic sampling algorithms for Bayesian networks typically deteriorates in face of extremely unlikely evidence. To address this problem, we propose the Ev…
A Robust Independence Test for Constraint-Based Learning of Causal Structure
Denver Dash, Marek J. Druzdzel
Constraint-based (CB) learning is a formalism for learning a causal network with a database D by performing a series of conditional-independence tests to infer structural informati…
Annealed MAP
Changhe Yuan, Tsai-Ching Lu, Marek J. Druzdzel
Maximum a Posteriori assignment (MAP) is the problem of finding the most probable instantiation of a set of variables given the partial evidence on the other variables in a Bayesia…
Importance Sampling in Bayesian Networks: An Influence-Based Approximation Strategy for Importance Functions
Changhe Yuan, Marek J. Druzdzel
One of the main problems of importance sampling in Bayesian networks is representation of the importance function, which should ideally be as close as possible to the posterior joi…
Learning Why Things Change: The Difference-Based Causality Learner
Mark Voortman, Denver Dash, Marek J. Druzdzel
In this paper, we present the Difference- Based Causality Learner (DBCL), an algorithm for learning a class of discrete-time dynamic models that represents all causation across tim…
AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks
J. Cheng, M. J. Druzdzel
Stochastic sampling algorithms, while an attractive alternative to exact algorithms in very large Bayesian network models, have been observed to perform poorly in evidential reason…