Kikuchi free energies with weak consistency constraints: change point learning in switching linear dynamical systems
published: Feb. 25, 2007, recorded: January 2005, views: 3004
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.
Exact inference in probabilistic models is often infeasible due to (i) a complicated conditional independence structure, and/or (ii) troublesome local integrals. Most challenging inference problems found in physics, such as the computation of the partition function in an Ising model or Boltzmann machine are examples of problems that suffer from a complex structure. All variables are binary, but the cycles in the model prevent an efficient recursive formulation of an inference algorithm.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !