Maximum-likelihood versus maximum a posteriori parameter estimation of physiological system models: the C-peptide impulse response case study.
Maximum-likelihood (ML), also given its connection to least-squares (LS), is widely adopted in parameter estimation of physiological system models, i.e., assigning numerical values to the unknown model parameters from the experimental data. A more sophisticated but less used approach is maximum a posteriori (MAP) estimation. Conceptually, while ML adopts a Fisherian approach, i.e., only experimental measurements are supplied to the estimator, MAP estimation is a Bayesian approach, i.e., a priori available statistical information on the unknown parameters is also exploited for their estimation. In this paper, after a brief review of the theory behind ML and MAP estimators, we compare their performance in the solution of a case study concerning the determination of the parameters of a sum of exponential model which describes the impulse response of C-peptide (CP), a key substance for reconstructing insulin secretion. The results show that MAP estimation always leads to parameter estimates with a precision (sometimes significantly) higher than that obtained through ML, at the cost of only a slightly worse fit. Thus, a three exponential model can be adopted to describe the CP impulse response model in place of the two exponential model usually identified in the literature by the ML/LS approach. Simulated case studies are also reported to evidence the importance of taking into account a priori information in a data poor situation, e.g., when a few or too noisy measurements are available. In conclusion, our results show that, when a priori information on the unknown model parameters is available, Bayes estimation can be of relevant interest, since it can significantly improve the precision of parameter estimates with respect to Fisher estimation. This may also allow the adoption of more complex models than those determinable by a Fisherian approach.
Sparacino
G
G
Department of Electronics and Informatics, University of Padova, Italy.
Tombolato
C
C
Cobelli
C
C
D001499:Bayes Theorem; D002096:C-Peptide / Q000008:administration & dosage / Q000097:blood / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D007328:Insulin / Q000378:metabolism; D000078790:Insulin Secretion; D016013:Likelihood Functions; D008954:Models, Biological; D016011:Normal Distribution; D012016:Reference Values; D013997:Time Factors
D002096:C-Peptide; D007328:Insulin