In this tutorial we demonstrate how one can use TKRISK to analyze vaccine response to COVID19. We create a simple model of contamination and show how vaccine and exposition can jointly affect the probability of being infected.
We review with this example the logic and applicability of Bayesian networks. We are able to closely match the analytical solution thanks to efficient sampling methods including low discrepancy sequences. This is the first of a three parts demo.
In this tutorial we demonstrate how one can use TKRISK to analyze vaccine response to COVID19. We create a simple model of contamination and show how vaccine and exposition can jointly affect the probability of being infected.
We review with this example the logic and applicability of Bayesian networks. We are able to closely match the analytical solution thanks to efficient sampling methods including low discrepancy sequences. This is the first of a three parts demo.
SAMPLING
PGM sampling is the core of our Bayesian Risk Systems. Due to its flexibility, scalability, this module support any kind of node type and distributions.
Features

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