Their estimates rely on contexts in the training data that are similar to (or in the same class as) the new sequence whose probability is to be estimated.
for example, in attempting to estimate a probability for the bigram black cloud, unseen in training, the transition probability associated with the class to which black belongs being followed by the class to which cloud belongs can be used.