More Information
On-demand content
● The talks in the course are pre-recorded and available to watch on-demand whenever is convenient. The talks for each session last up to 90 minutes in total.
● The computer practical sessions (around 1.5-3 hours) can be completed whenever is convenient. Full solutions are available to participants.
Live interactive sessions
There is an associated live online drop-in session for each half-day to come and ask questions (1pm-2.00pm UK time each day). Specifically these will be held on:
● Session 1: Friday 15th November 2024
● Session 2: Monday 18th November 2024
● Session 3: Wednesday 20th November 2024
● Session 4: Friday 22nd November 2024
● Session 5: Monday 25th November 2024
● Session 6: Wednesday 27th November 2024
● Session 7: Friday 29th November 2024
These are not compulsory, but are supplementary to the core content, and are an opportunity to engage with the course tutors.
Questions can also be asked during the course on a dedicated Slack channel.
Target audience and prerequisites
The target audience of the course is statisticians and people doing statistical analysis in any subject area.
No experience of Bayesian methods is assumed, but we do we assume familiarity with key statistical concepts:
● Basic probability concepts: discrete and continuous random variables; probability density functions; expectation; variance; familiarity with standard probability distributions (e.g. normal, binomial, uniform).
● A good understanding of classical (ie non-Bayesian) statistical modelling: likelihood (as in maximum likelihood estimation) and sampling distributions; linear regression; generalised linear models including logistic regression; assessment of model fit using residuals.
No experience with specialist Bayesian software will be assumed, but you should be comfortable using the statistical software R
While all code is provided, we think that you will find it easier to follow if you have a good familiarity with R, and we may not be able to fully support users with no R experience.
Course learning outcomes
1. Understand the principles of Bayesian statistics: learning from data and judgements through probability distributions on parameters in models.
2. Design a range of Bayesian models for health science problems, including appropriate selection of prior distributions.
3. Implement the models in standard software, and assess the performance of computational algorithms used for this.
4. Summarise and accurately interpret the output of Bayesian analyses.
5. Understand the assumptions being made in Bayesian models, and effectively appraise and compare them both qualitatively and quantitatively using standard methods.
Course tutors
Dr Anne Presanis – MRC Biostatistics Unit
Dr Robert Goudie – MRC Biostatistics Unit
Dr Christopher Jackson – MRC Biostatistics Unit
