Exponential Families of Stochastic Processes Natural Exponential Families of Léevy Processes Definitions and Examples First Properties Random Time Transformations Exponential Families of Markov Processes The Envelope Families Likelihood Theory Linear Stochastic Differential Equations with Time Delay Sequential Methods The Semimartingale Approach Alternative Definitions. Exponential families of stochastic processes are parametric stochastic p cess models for which the likelihood function exists at all nite times and has an exponential representation where the dimension of the canonical statistic is nite and independent of time. This denition not only covers manypracticallyimportantstochasticprocessmodels italsogivesrisetoa rather rich theory. This book aims at showing both aspects of exponential families of stochastic processes. Exponential families of stochastic processes are tractable from an a lytical as well as a probabilistic point of view. Therefore and because the theory covers many important models they form a good starting point for an investigation of the statistics of stochastic processes and cast interesting light on basic inference problems for stochastic processes. Exponential models play a central role in classical statistical theory for independent observations where it has often turned out to be informative and advantageous to view statistical problems from the general perspective of exponential families rather than studying individually specic expon tial families of probability distributions. The same is true of stochastic process models. Thus several published results on the statistics of parti lar process models can be presented in a unied way within the framework of exponential families of stochastic processes.