  This note continues study of exchangeability martingales, i.e., processes
that are martingales under any exchangeable distribution for the observations.
Such processes can be used for detecting violations of the IID assumption,
which is commonly made in machine learning. Violations of the IID assumption
are sometimes referred to as dataset shift, and dataset shift is sometimes
subdivided into concept shift, covariate shift, etc. Our primary interest is in
concept shift, but we will also discuss exchangeability martingales that
decompose perfectly into two components one of which detects concept shift and
the other detects what we call label shift. Our methods will be based on
techniques of conformal prediction.
