The correlation between disease pathology and tissue stiffness can be exploited to detect and potentially diagnose abnormal tissue states. Elastography is an imaging modality that attempts to image tissue stiffness by measuring local displacements caused by an applied force and calculating a strain map. Some elasticity imaging techniques attempt to assign a material parameter, such as Young's or shear modulus, to the imaged region in an effort to increase specificity. Unfortunately, the inversion techniques require many simplifying assumptions which lead to errors in the parameter estimates. One possible solution to increase accuracy in estimation is to first build an empirical model of the tissue using measured force-displacement data, thus eliminating the need for a priori assumptions. We propose the use of informational models for this purpose.