Network Sampling

Forward sample graph models

PGM sampling is the core of our Bayesian Risk Systems. This module offers a fast, flexible and scalable computation of the conditional probability distributions over the variables of interest for any kind of node type and distributions.



  • Multiple node types: Categorical, Discrete, Continuous, Mixture, Deterministic.

  • Exhaustive list of Discrete Distributions including: Binomial, Geometric, Hypergeometric, Logseries and Poisson.

  • Exhaustive list of Continuous Distributions including: Beta, Cauchy, Chisquare, Exponential, Gamma, Gumbel, Laplace, Logistic, Lognormal, Non Central Chisquare, Normal, Pareto, Power, Rayleigh, Student-t, Triangular, Uniform, Vonmises, Wald and Weibull.

  • Expression Parser for custom formulas combining operations on nodes.

  • Support for hybrid networks.

  • Export sampled graph values.

  • Multi step simulation for hybrid networks.


  • Koller D, Friedman N. Probabilistic graphical models: principles and techniques. MIT press; 2009.
  • Sucar LE. Probabilistic graphical models. Advances in Computer Vision and Pattern Recognition. London: Springer London. doi. 2015;10(978):1.
  • Bessière P, Mazer E, Ahuactzin JM, Mekhnacha K. Bayesian programming. CRC press; 2013 Dec 20.
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