#1




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




Re: Dependent Data
Unfortunately, there is no easy way to deal with dependent data even if they are generated by a Markov chain.
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#3




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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