Nonparametric identification of population models: an MCMC approach.
IEEE Trans Biomed Eng
; 55(1): 41-50, 2008 Jan.
Article
in En
| MEDLINE
| ID: mdl-18232345
ABSTRACT
The paper deals with the nonparametric identification of population models, that is models that explain jointly the behavior of different subjects drawn from a population, e.g., responses of different patients to a drug. The average response of the population and the individual responses are modeled as continuous-time Gaussian processes with unknown hyperparameters. Within a Bayesian paradigm, the posterior expectation and variance of both the average and individual curves are computed by means of a Markov Chain Monte Carlo scheme. The model and the estimation procedure are tested on both simulated and experimental pharmacokinetic data.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
Population Dynamics
/
Monte Carlo Method
/
Markov Chains
/
Models, Statistical
/
Models, Biological
Type of study:
Diagnostic_studies
/
Health_economic_evaluation
/
Risk_factors_studies
Limits:
Animals
/
Humans
Language:
En
Journal:
IEEE Trans Biomed Eng
Year:
2008
Document type:
Article
Affiliation country:
Italy