# Bayesian Statistics with R

The Bayesian framework represents an alternative approach to statistical modelling. Bayesian analysis describes how to update our initially incomplete "prior" knowledge with experimental observations (the "evidence") to obtain a better understanding (the "posterior") of the phenomenon studied. This concept fits very well the way how scientific research works.

In addition, Bayesian Networks represent an important step towards analysing
**causal relationships** which traditional statistical methods cannot model.

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

**Instructor:** András Aszódi.

## 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 group phenotypes: estimating several parameters at once.
- Blood group genotypes: modelling complex distributions with Bayesian MCMC techniques.
- 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.