Extracting information from data, often also called data analysis, is an important scientific task. Statistical approaches, which use methods from probability theory and numerical analysis, are well- founded but difficult to implement: the development of a statistical data analysis program for any given application is time-consuming and re- quires knowledge and experience in several areas. In this paper, we describe AutoBayes, a high-level generator system for data analysis programs from statistical models. A statistical model specifies the properties for each problem variable (i.e., observation or parameter) and its dependencies in the form of a probability distribu- tion. It is thus a fully declarative problem description, similar in spirit to a set of diffierential equations. From this model, AutoBayes generates optimized and fully commented C/C++ code which can be linked dy- namically into the Matlab and Octave environments. Code is generated by schema-guided deductive synthesis. A schema consists of a code tem- plate and applicability constraints which are checked against the model during synthesis using theorem proving technology. AutoBayes aug- ments schema-guided synthesis by symbolic-algebraic computation and can thus derive closed-form solutions for many problems. In this pa- per, we outline the AutoBayes system, its theoretical foundations in Bayesian probability theory, and its application by means of a detailed example.