Sparsistent Estimation of Time-Varying Discrete Markov Random Fields

Statistics – Machine Learning

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

We study a nonparametric method that estimates the structure of a discrete undirected graphical model from data. We assume that the distribution generating the data smoothly evolves over time and that the given sample is not identically distributed. Under the assumption that the underlying graphical model is sparse, the method recovers the structure consistently in the high dimensional, low sample size setting.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Sparsistent Estimation of Time-Varying Discrete Markov Random Fields does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Sparsistent Estimation of Time-Varying Discrete Markov Random Fields, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Sparsistent Estimation of Time-Varying Discrete Markov Random Fields will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-331751

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.