ABSTRACT
Probability is too important to be left to the experts.
–Richard Hamming, 1991.
Probability enters algorithmics in two ways: To account for the variability of data
presented to algorithms, and in randomized algorithms, where the action is driven
not only by the data, but also by explicit appeal to random events, such as simulation
of flipping coins or generating random numbers. We encounter randomness, and the
need for probabilistic reasoning, in both roles.
