This chapter introduces a class of graphical Markov models broadly called regression graph models. In general all graphical models are defined by specific conditional independence constraints in a system of random variables, constraints that have a precise graph representation. These models are useful to specify stepwise data generating processes and research hypotheses in cohort studies. We give some definitions and properties common to all graphical models, and then we discuss some examples. We will explain in some detail the case of a system of Gaussian variables.