Introduction to Bayesian Statistics (in-person)
Ben Lambert
All DPhil students Research staff Statistics
Friday, 26 January 2024, 9.30am to 5pm
This course aims to provide solid foundations to implementing linear regression, three types of Generalised Linear Models, and hierarchical models.
COURSE DETAILS
There are some ideas we know are true, and others we know are false. But for those ideas where we cannot be certain whether they are true of false, we need to use the language of probability.
Bayesian inference uses probability theory to allow us to update our uncertain beliefs in the light of data. It is increasingly used across the sciences and so a working knowledge of Bayesian statistics is essential for science researchers.
This course aims to provide a core understanding of Bayesian statistics that is grounded in mathematical theory, yet accessible to the less mathematically minded participants.
LEARNING OUTCOMES
By the end of the session participants will be able to:
• Develop a core understanding of Bayesian inference.
• Critically assess a statistical model.
INTENDED FOr
All
Additional Information
COURSE PRE-REQUISITE
A basic knowledge of a mathematical programming language, for example: R, Matlab, Python, Mathematica, or C++
ALSO PARTICIPANTS WILL NEED TO BRING LAPTOPS
Number of PLaces
30
COURSE LEADER
Ben Lambert
TIME / Date
09.30 - 17.00 | 26th January 2024
BOOKING INFORMATION
Book your place on 'Introduction to Bayesian statistics' by clicking here.
TERMS & CONDITIONS
When registering for this course, please check our Terms and Conditions.