This chapter introduces the use of Bayesian methodology for inversion purposes: the extraction of bio-geophysical parameters from remotely sensed data. Multisources information, such as different polarizations, frequencies, and sensors are fundamental to the development of operationally useful inversion systems. In this context, Bayesian methodologies offer a convenient tool of combining two or more disparate sources of information, models, and data. The chapter describes the development of a general model starting from a theoretical model, including the sensor noise and the model errors, by using a Bayesian approach. Furthermore, the developed procedure is applied to some experimental data sets. The author hopes that considering theoretical models and experimental data in many different configurations can give an idea of the versatility and robustness of the Bayesian framework.