An introduction to probability, emphasizing the combined use of mathematics and programming. Discrete and continuous families of distributions. Bounds and approximations. Transforms and convergence. Markov chains and Markov Chain Monte Carlo. Dependence, conditioning, Bayesian methods. The multivariate normal, random permutations, symmetry, and order statistics. Use of numerical computation, graphics, simulation, and computer algebra.