Nonparametric Learning of Switching Autoregressive Processes
published: Aug. 4, 2008, recorded: July 2008, views: 5988
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.
Vector autoregressive (VAR) processes are useful in describing dynamical phenomena as diverse as speech, financial time-series, and the dancing of honey bees. However, such phenomena often exhibit structural changes over time and the VAR which describe them must also change. For example, the vocal tract of a speaker contracts; a country experiences a recession, a central bank intervention, or some national or global event; a honey bee changes from a waggle to a turn right dance. Some of these changes will appear fre- quently, while others are only rarely observed. In ad- dition, there is always the possibility of a previously unseen dynamic behavior. Thus, we propose a non- parametric approach for learning switching VAR pro- cesses, where we take the state sequence to be Markov....
Download slides: icml08_fox_nls_01.pdf (1.7 MB)
Download slides: icml08_fox_nls_01.ppt (2.6 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !
Reviews and comments:
The sound is terrible but the presentation is solid. Nice work Emily.
Write your own review or comment: