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Stochastic Simulation Inference Algorithm in Restricted Pair Copula Bayesian Network with Single Root Node

In: Statistical Dependence Modeling

Author

Listed:
  • Panagiotis Basiouras Serrano

    (TU Delft, DIAM)

  • Dorota Kurowicka

    (TU Delft, DIAM)

Abstract

Bayesian Networks (BNs) are graphical models used to represent joint distributions. In this paper, we discuss copula-based BNs, where relationships between each node and its parents are defined through bivariate and conditional bivariate copulas, along with marginal densities. Under specific constraints on the graph structure, these models can be efficiently estimated and sampled (without the need for expensive integration). We focus on the problem of conditionalization in copula-based BNs, considering a network with a single root. The conditional distribution of a subset of nodes, given observed evidence from other nodes, is computed via sampling. Through a simulation study, we demonstrate that this approach is feasible for sufficiently sparse networks.

Suggested Citation

  • Panagiotis Basiouras Serrano & Dorota Kurowicka, 2026. "Stochastic Simulation Inference Algorithm in Restricted Pair Copula Bayesian Network with Single Root Node," Springer Books, in: Thomas Nagler & Dorota Kurowicka & Roger Cooke & Harry Joe (ed.), Statistical Dependence Modeling, pages 21-51, Springer.
    • Handle: RePEc:spr:sprchp:978-3-032-14252-8_3
    DOI: 10.1007/978-3-032-14252-8_3
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