Part 7 A preliminary survey: heads and tails - preliminary considerations; heads and tails - the random process; laws of "large numbers"; the "central limit theorem". Part 8 Random processes with independent increments: the case of asymptotic normality; the Wiener-Levy process; behaviour and asymptotic behaviour; ruin problems; ballot problems. Part 9 An introduction to other types of stochastic process: Markov processes; stationary processes. Part 10 Problems in higher dimensions: second-order characteristics and the normal distribution; the discrete case; the continuous case; the case of spherical symmetry. Part 11 Inductive reasoning, statistical inference: the basic formulation and preliminary clarifications; the case of independence and the case of dependence; exchangeability. Part 12 Mathematical statistics: the scope and limits of the treatment; the likelihood principle and sufficient statistics; a Bayesian approach to "estimation" and "hypothesis testing"; the connections with decision theory.