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.
EXACT INFERENCE
Exact inference is a solution for a fast implementation through the analytical computation of the conditional probability distributions over the variables of interest
Features

Support for hybrid networks

Implementation of Junction Trees Algorithm

Implementation of Elimination Trees Algorithm

Fast inference results
References
[1] Koller D, Friedman N. Probabilistic graphical models: principles and techniques. MIT press; 2009.  II Inference, p285
[2] Cowell RG, Dawid P, Lauritzen SL, Spiegelhalter DJ. Probabilistic networks and expert systems: Exact computational methods for Bayesian networks. Springer Science & Business Media; 2006 May 29.
[3] Maathuis M, Drton M, Lauritzen S, Wainwright M, editors. Handbook of graphical models. CRC Press; 2018 Nov 12.
[4] Cowell RG, Boutilier C. Local Propagation in Conditional Gaussian Bayesian Networks. Journal of Machine Learning Research. 2005 Sep 1;6(9).
[5] Darwiche A. Modeling and reasoning with Bayesian networks. Cambridge university press; 2009 Apr 6.