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.
Online exercises are available when this course is running. Please select
the option “Bayes” from the dropdown in the “Request an exercise notebook” form.
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.
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.
Number of participants: minimum 5, maximum 10.
Length: The course takes one half-day,
from 09:00 to 13:00 with 2 breaks.
Please provide affiliated Organisation, and your use-case or resource use estimation (CPU-hours, GPU-hours, Memory)