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.
DISTRIBUTION FITTER
Tenokonda Distribution Fitter returns the most likely distribution from a set of input observations.
Features
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Ranking of most likely distributions from over 30 different distributions
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Handles lower/upper bound truncated distributions
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Choice of goodness of fit measures
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Flexible constraints handling on any parameters
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Specific penalty weight setting
References
[1] H. Cramér, "Mathematical methods of statistics" , Princeton Univ. Press (1946)
[2] Claeskens, G.; Hjort, N. L. (2008), Model Selection and Model Averaging, Cambridge University Press
[3] Huber-Carol, C.; Balakrishnan, N.; Nikulin, M. S.; Mesbah, M., eds. (2002), Goodness-of-Fit Tests and Model Validity, Springer
[4] Cressie, N. and Read, T. R. C., “Multinomial Goodness-of-Fit Tests”, J. Royal Stat. Soc. Series B, Vol. 46, No. 3 (1984), pp. 440-464.