# 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.

## MODULES

At tenokonda, we build modular, scalable solutions that can be used out of the box or customized to your own needs.

Using our interactive user interface, your can create your own graphs. You have the ability to sample and analyse on the fly or save them for later use.

Including our PGM Graph Theory module will speed up and optimize several parts of the risk analysis

Exact inference is a solution for a fast implementation through the analytical computation of the conditional probability distributions over the variables of interest

PGM sampling is the core of our Bayesian Risk Systems. Due to its flexibility, scalability, this module supports any kind of node type and distribution.

After creating a graph, conditional probability distribution parameters can be calibrated using historical data or reflecting expert judgement.

When the structure of a graph is not known or one is interested to discover dependencies through available data, you can use our structure learning module.

What-if on the graph is available via the Sensitivity Analysis module.

Tenokonda Random Number Generator enables uniform quasi random numbers generation. It is based on Sobol low discrepancy sequences generators enhancing statistical properties of the resulting uniform distributions and in turn any distribution that can be sampled from a uniform through inverse transform.

Tenokonda Scenario Engine allows for random variables and processes modeling and simulation. Its role is fundamental in risk analysis as joint risk factors simulation allows in turn estimating tail metrics such as value at risk or expected shortfall.

Tenokonda Distribution Fitter returns the most likely distribution from a set of input observations.