Independently distributed and identically distributed (i.i.d): Difference between revisions
Xinreality (talk | contribs) Created page with "==Introduction== In machine learning, independently distributed and identically distributed (i.i.d). This is a statistical assumption that data points in a dataset are indepen..." |
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If we have a dataset of n samples denoted by X = [x1, xn], and a probability distribution function (pdf] denoted P, then the i.i.d assumption is that: | If we have a dataset of n samples denoted by X = [x1, xn], and a probability distribution function (pdf] denoted P, then the i.i.d assumption is that: | ||
*Each sample xi comes from the distribution P. | |||
*The samples are identically distributed. This means that they are drawn from the exact same distribution. | |||
==Applications in Machine Learning== | ==Applications in Machine Learning== | ||