Trust modelling is widely recognized as an aspect of essential importance in the construction of agents and multi agent systems (MAS). As a consequence, several trust formalisms have been developed over the last years. All of them have, in our opinion a limitation: they can determine the trustworthiness or untrustworthiness of the assertions expressed by a given agent, but they don't supply mechanisms for correcting this information in order to extract some utility from it. In order to overcome this limitation, we introduce the concept of reliability as a generalization of trust, and present Fuzzy Contextual Corrective Filters (FCCF) as reliability modeling methods loosely based on system identification and signal processing techniques. In order to prove their usefulness, we study their applicability to the appraisal variance estimation problem in the Agent Reputation and Trust (ART) testbed.