Model Selection in Markovian Processes

author: Shie Mannor, Department of Electrical Engineering, Technion - Israel Institute of Technology
published: Jan. 25, 2012,   recorded: December 2011,   views: 247
Categories

Slides

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.
  Bibliography

Description

We address the problem of how to use a sample of trajectories to choose from a candidate set of possible state spaces in different types of Markov processes. Standard approaches to solving this problem for static models use penalized maximum likelihood criteria that take the likelihood of the trajectory into account. Surprisingly, these criteria do not work even for simple fully observable finite Markov processes. We propose an alternative criterion and show that it is consistent. We then provide a guarantee on its performance with finite samples and illustrate its accuracy using simulated data and real-world data. We finally address the question of model selection in Markov decision processes, where the decision maker can actively select actions to assist in model selection.

See Also:

Download slides icon Download slides: nipsworkshops2011_mannor_markovian_01.pdf (2.4┬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 !

Write your own review or comment:

make sure you have javascript enabled or clear this field: