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Abstract Details
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Activity Number:
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345
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #303375 |
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Title:
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Parameter Estimation for Differential Equation Models: A Bayesian Approach
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Author(s):
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Kashyap Gupta*+
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Companies:
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Texas A & M University
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Address:
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Department of Statistics,Texas A & M University, College Station, TX, 77843,
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Keywords:
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Hierarchical Bayesian Model ;
Non Parametric Regression ;
Differential Equation ;
Inverse problem
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Abstract:
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Differential equations have a wide variety of applications in describing dynamic systems applied to scientific fields such as physics,engineering,economics and biomedical sciences. From a statistical point of view the inverse problem, using the measurements of state variables to estimate the parameters which characterize the system has not been explored much. Existing statistical methods which estimate parameters in differential equation models are frequentist in nature mainly dealing with plug in estimates of the state variables and its derivatives. In this paper we develop a hierarchical Bayesian model using traditional non-parametric regression techniques and a framework of measurement error. The proposed method and relevant results are applied using a flexible model called the FitzHugh-Nagumo model and an illustrative example has been presented.
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Authors who are presenting talks have a * after their name.
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