by András Aszódi

# Bayesian Statistics with R

"What is this 'Bayesian' thing? And why would I need to learn about it?"

You should learn about the Bayesian approach because it fits very well the way we do science. We update our incomplete "prior" knowledge with experimental observations (the "evidence") to obtain a better understanding (the "posterior") of the phenomenon studied.

In addition, Bayesian Networks represent an important step towards analysing causal relationships with statistical methods -- the foundation of "Machine Learning", or, for hype's sake, "Artificial Intelligence".

This course teaches you the principles of Bayesian statistics with hands-on practical examples in R.

## Topics

• Diagnostic kits, or: Calculating the probability of being sick if you tested positive, using Bayes' Theorem.
• Life and death of a mouse, or: Estimating the probability parameter of the Binomial distribution. Conjugate priors, updating the posterior.
• Blood groups, or: From phenotype to genotype. Modelling hidden variables the Bayesian way.
• Towards modelling causality: Introduction to Bayesian Networks.

## Exercises

Online exercises are available when this course is running. Please select the option "Bayes" from the dropdown in the "Request an exercise notebook" form.

## Out of scope

We cannot go into the specific data analysis problems of your particular project.

Note that "Bayesian statistics" is a huge subject and this introductory course only teaches you the basics.

## Prerequisites

Good knowledge of how to calculate probabilities and familiarity with discrete and continuous distributions are required. If you attended our Think Statistics! with R training then you are well prepared.

In addition, some basic familiarity with R is required. Our R as a programming language training provides a good foundation.

## Practical information

Number of participants: minimum 5, maximum 10.

Length: The course takes one half-day, from 09:00 to 13:00 with 2 breaks.