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.
MODEL CALIBRATION
After creating a graph, conditional probability distribution parameters can be calibrated using historical data or reflecting expert judgement.
Features
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Several optimization techniques available.
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Start simple with regression under linear assumptions on node dependencies.
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Incorporate expert judgment in parameterizing nodes distributions.
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Data imputation techniques available for incomplete datasets.
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Frequency matching.
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Preprocessing methods available prior to calibration.
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Data transformation routines. Node types: Categorical, Discrete, Continuous, Mixture, Deterministic.
References
[1] Koller D, Friedman N. Probabilistic graphical models: principles and techniques. MIT press; 2009. - III Learning - 17. Parameter Estimation p717
[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