Getting Started with Bayesian Statistics
In person
This two-class course will introduce you to working with Bayesian Statistics. Distinct from frequentist statistics, which is concerned with accepting or rejecting the null hypothesis, Bayesian Statistics asks what the probability of different hypotheses is, given the data and our prior beliefs about the world.
On this course, we will talk through the conceptual underpinnings of Bayesian Statistics, and give you hands-on practice fitting Bayesian models in R.
Please note that there is a reasonable amount of pre-setup required for this course, which in some cases will involve uninstalling and reinstalling R and RStudio (depending on version).
Instructions will be provided ahead of the training to those that have registered.
This course will be taught by Ponrawee Prasertsom.
After taking part in this event, you may decide that you need some further help in applying what you have learnt to your research. If so, you can book a Data Surgery meeting with one of our training fellows.
More details about Data Surgeries.
Those who have registered to take part will receive an email with full details on how to get ready for this course.
If you’re new to this training event format, or to CDCS training events in general, read more on what to expect from CDCS training. Here you will also find details of our cancellation and no-show policy, which applies to this event.
Level
This workshop requires the following pre-knowledge:
- This is an advanced-level course. We will assume that you are comfortable using R and RStudio
- Familiar with linear regression models (e.g. in lme4)
Learning Outcomes
- Understand how beliefs about the world are formalised
- See how different priors influence a model’s estimates
- Get to grips with interpreting posterior distributions
- Familiarise yourself with the workflow for running a Bayesian analysis
- Practice fitting and inspecting Bayesian models using the brms package in R
Skills
- Fitting Bayesian models in R using the brms package
- Evaluating and interpreting Bayesian model components
- Applying Bayesian reasoning to research questions
Explore More Training
- A Gentle Introduction to Causal Inference
- Data Analysis Workflow Design
- Digital Method of the Month: Machine Learning
- Digital Method of the Month. Statistics: How to pick the right method
- Explainable Machine Learning (XAI)
- Foundations of Machine Learning
- Getting Started with Data Analysis in Python
- Getting Started with Regression in R
- Linear Mixed Effects Modelling
Return to the Training Homepage to see other available events
Room 4.35, Edinburgh Futures Institute
This room is on Level 4, in the North East side of the building.
When you enter via the level 2 East entrance on Middle Meadow Walk, the room will be on the 4th floor straight ahead.
When you enter via the level 2 North entrance on Lauriston Place underneath the clock tower, the room will be on the 4th floor to your left.
When you enter via the level 0 South entrance on Porters Walk (opposite Tribe Yoga), the room will be on the 4th floor to your right.
