Dependent Data

[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'<N and then work with the sample if it were independent of size N'?

[magdon]

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

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'<N and then work with the sample if it were independent of size N'?

[data_user]

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