scholarly journals Study of Parameter Estimation and Model Calibration Using Bayesian Analysis of Noisy Data for a Virus Model

2017 ◽  
Author(s):  
Alejandro Mejia
Keyword(s):  
Parameter Estimation ◽  
Bayesian Analysis ◽  
Model Calibration ◽  
Noisy Data ◽  
Virus Model

scholarly journals Parameter Estimation by Picard-Iteration for Biochemical Networks with Noisy Data

2018 ◽  
Vol 51 (19) ◽  
pp. 64-67
Author(s):  
Maike Naber ◽  
Friedrich von Haeseler ◽  
Nadine Rudolph ◽  
Heinrich J. Huber ◽  
Rolf Findeisen
Keyword(s):  
Parameter Estimation ◽  
Noisy Data ◽  
Biochemical Networks ◽  
Picard Iteration

Parameter estimation of linear systems with input-output noisy data: A generalized lp norm approach

1994 ◽  
Vol 37 (3) ◽  
pp. 345-356 ◽  
Author(s):  
Jeng-Ming Chen ◽  
Bor-Sen Chen ◽  
Wei-Sheng Chang
Keyword(s):  
Parameter Estimation ◽  
Linear Systems ◽  
Noisy Data ◽  
Input Output ◽  
Lp Norm

scholarly journals Two-qubit Hamiltonian tomography by Bayesian analysis of noisy data

2009 ◽  
Vol 80 (2) ◽  
Author(s):  
Sonia G. Schirmer ◽  
Daniel K. L. Oi
Keyword(s):  
Bayesian Analysis ◽  
Noisy Data

scholarly journals Joint state-parameter estimation of a nonlinear stochastic energy balance model from sparse noisy data

2019 ◽  
Vol 26 (3) ◽  
pp. 227-250 ◽  
Author(s):  
Fei Lu ◽  
Nils Weitzel ◽  
Adam H. Monahan
Keyword(s):  
Parameter Estimation ◽  
Energy Balance ◽  
Noisy Data ◽  
State Parameter ◽  
Energy Balance Model ◽  
Balance Model ◽  
Spatiotemporal Modeling ◽  
Mcmc Method ◽  
Physical Constraints ◽  
Observation Noise

Abstract. While nonlinear stochastic partial differential equations arise naturally in spatiotemporal modeling, inference for such systems often faces two major challenges: sparse noisy data and ill-posedness of the inverse problem of parameter estimation. To overcome the challenges, we introduce a strongly regularized posterior by normalizing the likelihood and by imposing physical constraints through priors of the parameters and states. We investigate joint parameter-state estimation by the regularized posterior in a physically motivated nonlinear stochastic energy balance model (SEBM) for paleoclimate reconstruction. The high-dimensional posterior is sampled by a particle Gibbs sampler that combines a Markov chain Monte Carlo (MCMC) method with an optimal particle filter exploiting the structure of the SEBM. In tests using either Gaussian or uniform priors based on the physical range of parameters, the regularized posteriors overcome the ill-posedness and lead to samples within physical ranges, quantifying the uncertainty in estimation. Due to the ill-posedness and the regularization, the posterior of parameters presents a relatively large uncertainty, and consequently, the maximum of the posterior, which is the minimizer in a variational approach, can have a large variation. In contrast, the posterior of states generally concentrates near the truth, substantially filtering out observation noise and reducing uncertainty in the unconstrained SEBM.

scholarly journals Bayesian analysis of galaxy spectral energy distributions with BayeSED

2012 ◽  
Vol 8 (S295) ◽  
pp. 312-312
Author(s):  
Yunkun Han ◽  
Zhanwen Han
Keyword(s):  
Parameter Estimation ◽  
Complex Systems ◽  
Bayesian Analysis ◽  
Bayesian Model ◽  
Model Comparison ◽  
Spectral Energy ◽  
Infrared Galaxies ◽  
Energy Distributions ◽  
Bayesian Model Comparison ◽  
Rigorous Test

AbstractIn Han & Han (2012), we have preliminarily built BayeSED and applied it to a sample of hyperluminous infrared galaxies. The physically reasonable results obtained from Bayesian model comparison and parameter estimation show that BayeSED could be a useful tool for understanding the nature of complex systems, such as dust obscured starburst-AGN composite galaxies, from decoding their complex SEDs. In this contribution, we present a more rigorous test of BayeSED by making a mock catalog from model SEDs with the value of all parameters to be known in advance.

scholarly journals Model calibration using the automatic parameter estimation procedure (PEST) of the North-eastern zone of the Milan Functional Urban Area (Italy)

2018 ◽  
Vol 7 (2) ◽  
Author(s):  
Luca Alberti
Keyword(s):  
Parameter Estimation ◽  
Urban Area ◽  
Model Calibration ◽  
Estimation Procedure ◽  
The North ◽  
Parameter Estimation Procedure ◽  
Eastern Zone ◽  
North Eastern

La Functional Urban Area (FUA) di Milano è un’area densamente popolata (2.254.263 abitanti) dove l’approvvigionamento idrico è garantito esclusivamente mediante prelievi idrici sotterranei. Per questa ragione la protezione della qualità delle falde rientra tra le priorità delle politiche ambientali di Regione Lombardia. Recentemente è stato avviato un programma di studi ed interventi aventi lo scopo d’individuare i principali plumes di contaminazione da solventi clorurati distinguendone l’impatto da quello legato all’inquinamento diffuso. In questo articolo si presenta il modello di flusso sviluppato per il settore NE della FUA di Milano, settore utilizzato quale area pilota per sviluppare e testare una nuova metodologia che combina statistica e modellistica al fine di distinguere il contributo delle fonti di contaminazione puntuale rispetto a quelle diffuse.

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