# 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.

## DEEP LEARNING / NLP / AI

SOLUTIONS

__Probabilistic Graphical Models__

Scenario Analysis on Climate Change, Pandemics (e.g. COVID-19), Credit Risk, Decision-Making under Uncertainty and other risk related topics are difficult domains to analyse. The analysis typically involve large collections of random variables with complex conditional dependency structures. Probabilistic Graphical Models (PGM) are a good fit to model such complexity as they bring together probability theory and graph theory through a fully interpretable structure. Our PGM library is a powerful, flexible framework that enables clients to run their analysis, incorporating expert knowledge.

__Scenario Engine__

Scenario simulation is the most ubiquitous approach to assess risk across a spectrum. Tenokonda Simulation Engine (TKSE) is an easy to use simulation package that helps you quantify the uncertainties associated with your models. It features:

- Probability distributions sampler

- Preprocessing Time series clean up and homogenization

- Correlation matrix estimation

- Joint Simulation of Monte Carlo scenario generation

- Codependency modeling through copulas

- Simple API (python)

- Dynamic scenario simulation of stochastic processes

__Random Number Generator__

Quasi-random number generator cover distribution support more quickly and evenly than traditional Monte Carlo methods (Mersenne Twister). Tenokonda has implemented a robust and fast Low Discrepancy Sequence (LDS) generator. It can be used to:

- Characterize probability density function (pdf) with a small number of simulations

- Provide starting points for optimization algorithms

- Numerical integration using collocation methods