Two issues in using mixtures of polynomials for inference in hybrid Bayesian networks

DOI10.1016/j.ijar.2012.01.008zbMath1446.62065OpenAlexW2042134289MaRDI QIDQ448955

Prakash P. Shenoy

Publication date: 11 September 2012

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijar.2012.01.008

zbMATH Keywords

Chebyshev pointsLagrange interpolating polynomialsconditional linear Gaussian distributionsconditional log-normal distributionsinference in hybrid Bayesian networksmixtures of polynomials

Mathematics Subject Classification ID

Bayesian inference (62F15) Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence (68T35) Approximations to statistical distributions (nonasymptotic) (62E17)

Related Items (4)

On conditional truncated densities Bayesian networks ⋮ Learning mixtures of polynomials of multidimensional probability densities from data using B-spline interpolation ⋮ Learning mixtures of truncated basis functions from data ⋮ A Framework for Solving Hybrid Influence Diagrams Containing Deterministic Conditional Distributions

Cites Work

• Inference in hybrid Bayesian networks using mixtures of polynomials
• Approximate probability propagation with mixtures of truncated exponentials
• Lagrange interpolation on Chebyshev points of two variables
• Importance sampling in Bayesian networks using probability trees.
• A Re-definition of Mixtures of Polynomials for Inference in Hybrid Bayesian Networks
• A Framework for Solving Hybrid Influence Diagrams Containing Deterministic Conditional Distributions
• On Information and Sufficiency
• Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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