In basic conformal prediction the goal is to predict the label of a test object {$x_{n+1}$} given a training set {$z_1,\ldots,z_n$}. In transductive conformal prediction, we are given a set {$x_{n+1},\ldots,x_{n+k}$} of test objects and the goal is to predict their labels without getting any intermediate feedback. This is motivated by the general problem of transduction in machine learning.