In this tutorial we demonstrate how one can use TKRISK to analyze vaccine response to COVID-19. 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 COVID-19. 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.
SENSITIVITY ANALYSIS
What-if on the graph is available via the Sensitivity Analysis module.
Features
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Impact analysis on a part of the graph toward a target node.
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Applying evidence to one or more nodes and resamples.
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Reverse Stress Testing.
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Graph queries.
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Retrieve marginals and joint distributions.
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Analyse distributions moments and quantiles.
References
[1] Koller D, Friedman N. Probabilistic graphical models: principles and techniques. MIT press; 2009.
[2] Nielsen TD, Jensen FV. Bayesian networks and decision graphs. Springer Science & Business Media; 2009 Mar 17. - 5 Analysis Tools for Bayesian Networks p167
[3] Darwiche A. Modeling and reasoning with Bayesian networks. Cambridge university press; 2009 Apr 6. - 16 Sensitivity Analysis p417