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Found 13 papers, 0 papers with code
Approximate Kalman Filter Q-Learning for Continuous State-Space MDPs
We seek to learn an effective policy for a Markov Decision Process (MDP) with continuous states via Q-Learning.
Evidence Absorption and Propagation through Evidence Reversals
The arc reversal/node reduction approach to probabilistic inference is extended to include the case of instantiated evidence by an operation called "evidence reversal."
Efficient Inference on Generalized Fault Diagrams
The generalized fault diagram, a data structure for failure analysis based on the influence diagram, is defined.
DAVID: Influence Diagram Processing System for the Macintosh
Influence diagrams are a directed graph representation for uncertainties as probabilities.
Intelligent Probabilistic Inference
This paper extends those results by developing a theory of the properties of the diagram that are used by the algorithm, and the information needed to solve arbitrary probability inference problems.
A Heuristic Bayesian Approach to Knowledge Acquisition: Application to Analysis of Tissue-Type Plasminogen Activator
This paper describes a heuristic Bayesian method for computing probability distributions from experimental data, based upon the multivariate normal form of the influence diagram.
Simulation Approaches to General Probabilistic Inference on Belief Networks
A number of algorithms have been developed to solve probabilistic inference problems on belief networks.
A Linear Approximation Method for Probabilistic Inference
An approximation method is presented for probabilistic inference with continuous random variables.
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.!
Directed Reduction Algorithms and Decomposable Graphs
In recent years, there have been intense research efforts to develop efficient methods for probabilistic inference in probabilistic influence diagrams or belief networks.
Laplace's Method Approximations for Probabilistic Inference in Belief Networks with Continuous Variables
Laplace's method, a family of asymptotic methods used to approximate integrals, is presented as a potential candidate for the tool box of techniques used for knowledge acquisition and probabilistic inference in belief networks with continuous variables.
A Decision-Based View of Causality
Using this definition, we show how causal dependence can be represented within an influence diagram.
A Definition and Graphical Representation for Causality
We present a precise definition of cause and effect in terms of a fundamental notion called unresponsiveness.
