CS-E4070 - Approximate Bayesian Computation: 2019 (3 ECTS)
Responsible Teacher: Neda B. Marvasti
Level of the Course: Master's and PhD level
Teaching Period: I-II (Autumn)
Description: Approximate Bayesian Computation (a.k.a. ABC, likelihood-free inference) is a new class of computational inference methods that can be used when the likelihood function is difficult to evaluate or unknown, and one has a simulator for generating data that (hopefully) resemble observations when generated with correct parameters. The underlying intuition is that similar model parameters are likely to generate similar data, but the practice is of course a bit more complex...
ABC has applications from medicine to particle physics, and is expected to revolutionize computional sciences that cannot apply traditional statistical methods.
Prerequisite: Solid background in statistics. Being familiar with bayesian inference methods.
In Sisson, S. A., In Fan, Y., & In Beaumont, M. A. (2019). Handbook of approximate Bayesian computation.Recommended toolbox:Toolbox for Likelihood free inference: ELFI
Check Syllabus for list of papers and detailed information on the course materials.