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#1
07-17-2012, 04:38 PM
 data_user Junior Member Join Date: Jul 2012 Posts: 6
Dependent Data

The independence of data seems to be curtail for both theoretical analysis and practical efficiency. What if the sample (x1,y1)...(xN,yN) consists of correlated points? For example, x1....xN is a realization of a Markov chain. Can we still learn from these data? Do we need to change the standard learning algorithms to account for the dependence? Is it possible to introduce a notion of "effective" number of data points N'<N and then work with the sample if it were independent of size N'?
#2
07-18-2012, 08:37 AM
 magdon RPI Join Date: Aug 2009 Location: Troy, NY, USA. Posts: 595
Re: Dependent Data

Unfortunately, there is no easy way to deal with dependent data even if they are generated by a Markov chain.

Quote:
 Originally Posted by data_user The independence of data seems to be curtail for both theoretical analysis and practical efficiency. What if the sample (x1,y1)...(xN,yN) consists of correlated points? For example, x1....xN is a realization of a Markov chain. Can we still learn from these data? Do we need to change the standard learning algorithms to account for the dependence? Is it possible to introduce a notion of "effective" number of data points N'
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#3
07-23-2012, 06:22 PM
 data_user Junior Member Join Date: Jul 2012 Posts: 6
Re: Dependent Data

This is a good source of references on the subject:
http://cscs.umich.edu/~crshalizi/not...-learning.html

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