Example for non-iid data

Question

I have read some papers regarding to non-iid data. Based on Wikipedia, I know what iid (independent and identical distributed) data is but am still confused about non-iid. I did some research but could not find a clear definition and example of it. Can someone help me on this?

Answer 1

From wikipedia iid:

"Independent and identically distributed" implies an element in the sequence is independent of the random variables that came before it. In this way, an IID sequence is different from a Markov sequence, where the probability distribution for the nth random variable is a function of the previous random variable in the sequence (for a first order Markov sequence).

As a simple synthetic example, assume you have a special dice with 6 faces. If the last time the face value is 1, next time you throw it, you will still get a face value of 1 with 0.5 probability and a face value of 2,3,4,5,6 each with 0.1 probability. However, if the last time the face value is not 1, you get equal probability of each face. E.g.,

p(face(0) = k) = 1/6, k = 1,2,3,4,5,6  -- > initial probability at time 0. 
p(face(t) = 1| face(t-1) = 1) = 0.5, p(face(t) = 1| face(t-1) != 1) = 1/6
p(face(t) = 2| face(t-1) = 1) = 0.1, p(face(t) = 1| face(t-1) != 1) = 1/6
p(face(t) = 3| face(t-1) = 1) = 0.1, p(face(t) = 1| face(t-1) != 1) = 1/6
p(face(t) = 4| face(t-1) = 1) = 0.1, p(face(t) = 1| face(t-1) != 1) = 1/6
p(face(t) = 5| face(t-1) = 1) = 0.1, p(face(t) = 1| face(t-1) != 1) = 1/6
p(face(t) = 6| face(t-1) = 1) = 0.1, p(face(t) = 1| face(t-1) != 1) = 1/6
face(t) stands for the face value of t-th throw.

This is an example when the probability distribution for the nth random variable (the result of the nth throw) is a function of the previous random variable in the sequence.

I see Non-identical and Non-independent (e.g, Markovian) data in some machine learning scenarios, which can be thought of as non-iid examples.

• Online learning with streaming data, when the distribution of the incoming examples changes over time: the examples are not identically distributed. Suppose you have a learning module for predicting the click-thru-rate of online-ads, the distribution of query terms coming from the users are changing during the year dependent on seasonal trending. The query terms in summer and in Christmas season should have different distribution.

• Active learning, where labels for specific data are requested by the learner: the independence assumption is also violated.

• Learning / making inference with graphical models. Variables are connected thru dependence relations.