Bayesian estimation of the analyte concentrations using the sensor responses and the design optimization of a sensor system

Using an array of sensors with well calibrated but different tuning curves, it is possible to appreciate a wide range of stimuli. In this work, we first revisit the statistical estimation of the stimuli concentrations given the responses of a sensor array, discussed in Sanchez-Montanes & Pearce [18]. Since it is not a typical regression problem, the Bayesian concept is adopted to develop an estimation method by elucidating the dynamic and uncertain nature of the environment-dependent stimuli with a proper choice of the probability distribution. Other studies confirm that the proposed method can demonstrate a superior performance in terms of accuracy and precision when compared to the popular frequentist methods in addition to the theoretical soundness it enjoys as a statistical estimation problem. Under the proposed framework, the design optimization of an artificial sensory system is also formulated using the expected Bayes risk as an objective function to minimize. The same approach may be equally applied to any sensory system in order to optimize its performance within a population of sensors. Finally, illustrative examples are provided to describe how the proposed method can be applied for the optimal configuration of a sensory system for a given sensing task.