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.
STRUCTURE LEARNING
You can use our structure learning module to build the structure of the graph or to discover dependencies through available data.
Features
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Nodes dependency discovery.
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Hypothesis testing.
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Model and variable selection.
References
[1] Koller D, Friedman N. Probabilistic graphical models: principles and techniques. MIT press; 2009. - III Learning - 18. Structure Learning in Bayesian Networks p783
[2] Sucar LE. Probabilistic graphical models. Advances in Computer Vision and Pattern Recognition. London: Springer London. doi. 2015;10(978):1.
[3] Darwiche A. Modeling and reasoning with Bayesian networks. Cambridge university press; 2009 Apr 6. - 17 Learning: The Maximum Likelihood Approach p439, 18 Learning: The Bayesian Approach p477
[4] Koller D, Friedman N, Džeroski S, Sutton C, McCallum A, Pfeffer A, Abbeel P, Wong MF, Meek C, Neville J, Jensen D. Introduction to statistical relational learning. MIT press; 2007.