Introduction to Bayesian Inference (IN-PERSON)

PrerequisitesUpdated

This course is aimed to provide the tools to conduct Bayesian inference in common situations.

This course is aimed to provide the tools to conduct Bayesian inference in common situations. We will be contrasting Bayesian Inference with classical hypothesis testing, covering conjugate distributions and credible intervals. We will also look at modern computational methods such as MCMC approaches using the RSTAN library.

If you do not have a University of Cambridge Raven account please book or register your interest here.

If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry.

• ♿ The training room is located on the first floor and there is currently no wheelchair or level access.
• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance. After you have booked a place, if you are unable to attend any of the live sessions, please email the Research Informatics Training Team.
• Further details regarding eligibility criteria are available here.
• Guidance on visiting Cambridge and finding accommodation is available here.

• Everyone is welcome to attend the courses, please review the relevant policies.

• Participants should be experienced in programming in R as the course will build on this. We recommend the Introduction to R course as a first course to start programming in R. If you are not able to attend an introductory course, please work through the R material as a minimum.
• Participants should also have a solid grounding in classical statistics at the level of the Core Statistics using R course. They should be familiar with basic probability theory including familiarity with basic properties such as density functions, expectation and variance of the normal and binomial distributions. Some basic calculus will be used, but it is not essential for understanding.

Number of sessions: 1

• Bayes Law applied to data
• Conjugate priors
• Contrasting Bayesian and classical approaches
• Bayesian credible intervals
• A brief introduction to MCMC
• Using RSTAN

• Developing an understanding of the pros and cons of Bayesian inference
• Understanding the principles of Bayesian inference by doing simple Bayesian calculations by hand.
• Building confidence in using computational methods on real world data using BUGS.

Presentations, demonstrations and practicals

Participants can make use of the computers in the training room.

This is subject to change in line with the training schedule.

• Free for registered University of Cambridge students
• £ 60/day for all University of Cambridge staff, including postdocs, temporary visitors (students and researchers) and participants from Affiliated Institutions. Please note that these charges are recovered by us at the Institutional level
• It remains the participant's responsibility to acquire prior approval from the relevant group leader, line manager or budget holder to attend the course. It is requested that people booking only do so with the agreement of the relevant party as costs will be charged back to your Lab Head or Group Supervisor.
• £ 60/day for all other academic participants from external Institutions and charitable organizations. These charges must be paid at registration
• £ 120/day for all Industry participants. These charges must be paid at registration
• Further details regarding the charging policy are available here

1

several times a year

• Introduction to R (ONLINE LIVE TRAINING)
• Core Statistics using R or Python (ONLINE LIVE TRAINING)