iid

i.i.d. assumption, iid

independent and identically distributed

Independent means one event’s outcome doesn’t provide any information about another event. Identical means that if a subset of data is sampled from different parts of the dataset, the distribution is the same (identical parameters)

Reverse of

Markov Chain sequence data.

Random Variables, when there are multiple, are mutually independent and all have the same

Probability Distribution

The n samples your observed are considered as random variables.

Identical distributed

Independently distributed

Clarifications on I.I.D. assumption in machine learning

In this question, it was stated that the assumption of i.i.d. for data comes in the form of $$(X_i,y_i)∼P(X,y),∀i=1,...,N \\(X_i,y_i) \;independent\; of \;(X_j,y_j),\;∀i≠j∈{1,...,N} $$ I am clear w...

https://stats.stackexchange.com/questions/546518/clarifications-on-i-i-d-assumption-in-machine-learning

iid

i.i.d. assumption, iid

independent and identically distributed

Identical distributed

Independently distributed

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