Latent Force Models with Gaussian Processes
published: Oct. 9, 2008, recorded: September 2008, views: 4931
Report a problem or upload filesIf 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.
We are used to dealing with the situation where we have a latent variable. Often we assume this latent variable to be independently drawn from a distribution, e.g. probabilistic PCA or factor analysis. This simplification is often extended for temporal data where tractable Markovian independence assumptions are used (e.g. Kalman filters or hidden Markov models). In this talk we will consider the more general case where the latent variable is a forcing function in a differential equation model. We will show how for some simple ordinary differential equations the latent variable can be dealt with analytically for particular Gaussian process priors over the latent force. In this talk we will introduce the general framework, present results in systems biology preview extensions.
Joint work with Magnus Rattray, Mauricio Alvarez, Pei Gao, Antti Honkela, David Luengo, Guido Sanguinetti and Michalis Titsias.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !