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Reversible Jump Markov Chain Monte Carlo for Deconvolution
- Source :
- Journal of Pharmacokinetics and Pharmacodynamics. 34:263-287
- Publication Year :
- 2007
- Publisher :
- Springer Science and Business Media LLC, 2007.
-
Abstract
- To solve the problem of estimating an unknown input function to a linear time invariant system we propose an adaptive non-parametric method based on reversible jump Markov chain Monte Carlo (RJMCMC). We use piecewise polynomial functions (splines) to represent the input function. The RJMCMC algorithm allows the exploration of a large space of competing models, in our case the collection of splines corresponding to alternative positions of breakpoints, and it is based on the specification of transition probabilities between the models. RJMCMC determines: the number and the position of the breakpoints, and the coefficients determining the shape of the spline, as well as the corresponding posterior distribution of breakpoints, number of breakpoints, coefficients and arbitrary statistics of interest associated with the estimation problem. Simulation studies show that the RJMCMC method can obtain accurate reconstructions of complex input functions, and obtains better results compared with standard non-parametric deconvolution methods. Applications to real data are also reported.
- Subjects :
- Mathematical optimization
Posterior probability
Models, Biological
LTI system theory
symbols.namesake
Cocaine
Humans
Computer Simulation
Pharmacokinetics
Mathematics
Pharmacology
Drug Administration Routes
Uncertainty
Reproducibility of Results
Bayes Theorem
Markov chain Monte Carlo
Reversible-jump Markov chain Monte Carlo
Inverse problem
Markov Chains
Spline (mathematics)
Linear Models
symbols
Piecewise
Deconvolution
Sulpiride
Monte Carlo Method
Algorithm
Algorithms
Subjects
Details
- ISSN :
- 15738744 and 1567567X
- Volume :
- 34
- Database :
- OpenAIRE
- Journal :
- Journal of Pharmacokinetics and Pharmacodynamics
- Accession number :
- edsair.doi.dedup.....fc947b7ee899c9948c50abb4331ebe43