Approximate Bayesian computation: a simulation based approach to inference

author: Richard Wilkinson, Department of Molecular Biology and Biotechnology, University of Sheffield
published: Sept. 9, 2008,   recorded: May 2008,   views: 9600


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


There is a large class of stochastic models for which we can simulate observations from the model, but for which the likelihood function is unknown. Without knowledge of the likelihood function standard inference techniques such as Markov Chain Monte Carlo are impossible, as the unnormalized likelihood function is explicitly required for the calculation of an acceptance rate. In this talk I shall introduce a group of Monte Carlo methods that can be used to perform inference for stochastic models from which we can cheaply simulate observations.

See Also:

Download slides icon Download slides: aispds08_wilkinson_abc_01.pdf (2.1┬áMB)

Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Reviews and comments:

Comment1 Will Hoppitt, March 31, 2009 at 11:31 a.m.:

This is really helpful and well explained, thank you!

Write your own review or comment:

make sure you have javascript enabled or clear this field: