# Independent and identically distributed samples [duplicate]

Say i am training a neural network and have 10 samples with 4 variables each and 1 label assigned to each observation. What does it mean to say that the samples are independent and identically distributed?

I know what IID variables are. And i have read statements such as "Because we usually assume that our samples are independent and identically distributed, the likelihood over all of our examples decomposes into a product over the likelihoods of individual examples: text omitted". What exactly does it mean for the samples (here 4 variables and 1 class label) to be iid?

• You have 10 iid draws. In general an observation both label and variables are one draw. Variables and label can be dependent in any way shape or form. If the label is not dependent on the variables it is pretty bad. Typically unbiased models are obtained if $P(y_i|X_i)=P(y_i|X,y_{not-i})$ since this is the relationship you use to derive the likehood function. – Peter Mølgaard Pallesen Jun 29 '17 at 8:04