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In dynamic settings, customer behavior is unpredictable, a circumstance that causes the process demand not to be identically distributed across process contexts over the planning horizon.
We can assume that, as biological replicates, the data should be identically distributed across channels, and so we performed quantile normalization on these data.
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Rather, for variables to be exchangeable their marginal distributions must be the same (i.e., they must be identically distributed).
A sequence ({X_{n}, ngeq1}) of random variables is named to be identically distributed if, for each (igeq1,X_{i}) and (X_{1}) are identically distributed.
Now they're identically distributed, so you just have to take the n-th power of that.
Moreover, if random variables are identically distributed, then (3.3).
If the branches are identically distributed ( and ), (13) reduces to.
Case 1. Channels gains from different transmit antennas to a common receive antenna are identically distributed, but the gains associated with different receive antennas are non-identically distributed.
Denote by (X'), (Y') two independent copies of X, Y respectively, i.e. X and (X') are identically distributed, Y and (Y') are identically distributed, and all X, (X'), Y, (Y') are mutually independent.
Case 2. Channels gains from a common transmit antenna to different receive antennas are identically distributed, but the gains associated with different transmit antennas are non-identically distributed.
If the groups are identically distributed, p = 1/2.
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