Existing works on stochastic processes belong to a field of abstract mathematics which puts them beyond the scope of the non-specialist. The preoccupations of research mathematicians being more often than not distant from the practical problems of experimental methodology, the needs of practical workers, though real, are not met by the majority of works that. deal with processes. By "practical workers", we mean research scientists in all the different disciplines: Physics, Chemistry, Biology, Medicine, Population, Economics, Organisation, Operational Research etc. Indeed, all scientific research today touches upon complex fields in which deterministic models can be useful for no more than an element ary and simple approximation. The Calculus of Probability although offering some interesting models is still inadequate in many instances, particularly in the study of evolving systems. The practical worker must therefore have at his disposal a set of original and varied stochastic models. These models must not be too general, for in that case not only would their theoretical study prove difficult, but above all the adaptation of such models to an observed system would lead to an estimation of a great number of parameters on the basis of a necessarily restricted sample. This would constitute an insuperable difficulty for the practical scientist. It is therefore essential for him to have at his disposal a varied range of very characteristic models.

Inhalt
I Introduction.- 1. Examples.- 2. Definitions.- 3. Classification according to the nature of E.- 4. Classification according to the nature of the set T of instants where the system is observed.- II Poisson processes.- A. Generalities Point processes.- B. The uniform Poisson process.- C. Probability distributions associated to Poisson processes.- D. Extensions: Poisson-type-processes.- Problems for solution.- Random functions associated to a Poisson process.- III Numerical processes with independent random increments.- 1. Definition.- 2. Mean values zero mean process.- 3. Variance distribution.- 4. Cumulants and second characteristic functions.- 5. Indicator function of a Poisson process.- 6. The Wiener-Levy process.- 7. Laplace processes with independent increments.- 8. General form of the random functions X (t) with independent increments.- 9. Infinitely divisible distributions.- 10. Stable distributions.- Problems for solution.- IV Markov processes.- A. Generalities and definitions.- B. Study by means of convexity.- C. Study by means of spectral analysis.- D. Direct algebraic study.- E. Reaching delays and sojourn duration problems.- F. Miscellaneous.- Problems for solution.- V Laplace processes and second order processes.- 1. Introduction.- A. Second order properties.- B. Laplace processes.- Problems for solution.- VI Some Markov processes on continuous-time.- 1. Homogeneous Laplace-Markov sequences.- 2. Stationary Laplace-Markov sequence.- 3. Estimation problems.- 4. Interpolation permanent process.- 5. Non homogeneous standardized Laplace-Markov processes.- 6. General form of Laplace-Markov processes.- 7. Wiener-Levy processes.- 8. Poisson-Markov processes.- Problems for solution.- Answers to problems.