A Bayesian Framework for Parameter Estimation in Dynamical Models
نویسندگان
چکیده
منابع مشابه
A Bayesian Framework for Parameter Estimation in Dynamical Models
Mathematical models in biology are powerful tools for the study and exploration of complex dynamics. Nevertheless, bringing theoretical results to an agreement with experimental observations involves acknowledging a great deal of uncertainty intrinsic to our theoretical representation of a real system. Proper handling of such uncertainties is key to the successful usage of models to predict exp...
متن کاملParameter estimation for nonlinear dynamical adjustment models
A recursive generalized least squares algorithm and a filtering based least squares algorithm are developed for input nonlinear dynamical adjustment models with memoryless nonlinear blocks followed by linear dynamical blocks. The basic idea is to use the filtering technique and to replace the unknown terms in the information vectors with their estimates. The simulation results show the performa...
متن کاملA Hierarchical Bayesian Approach for Parameter Estimation in HIV Models
A hierarchical Bayesian approach is developed to estimate parameters at both the individual and the population level in a HIV model, with the implementation carried out by Markov Chain Monte Carlo (MCMC) techniques. Sample numerical simulations and statistical results are provided to demonstrate the feasibility of this approach.
متن کاملBehavioral game theoretic models: a Bayesian framework for parameter analysis
Studies in experimental economics have consistently demonstrated that Nash equilibrium is a poor description of human players’ behavior in unrepeated normal-form games. Behavioral game theory offers alternative models that more accurately describe human behavior in these settings. These models typically depend upon the values of exogenous parameters, which are estimated based on experimental da...
متن کاملBayesian state and parameter estimation of uncertain dynamical systems
The focus of this paper is Bayesian state and parameter estimation using nonlinear models. A recently developed method, the particle filter, is studied that is based on stochastic simulation. Unlike the well-known extended Kalman filter, the particle filter is applicable to highly nonlinear models with non-Gaussian uncertainties. Recently developed techniques that improve the convergence of the...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PLoS ONE
سال: 2011
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0019616