Dec 1, 2006 · This approach can be problematic if parameter estimates are correlated or if model structure does not permit obvious standard error estimates.
Hazen and Huang: Parametric Sensitivity Analysis Using Large-Sample Approximate Bayesian Posterior Distributions. Decision Analysis 3(4), pp. 208–219, © 2006 ...
Dive into the research topics of 'Parametric Sensitivity Analysis Using Large-Sample Approximate Bayesian Posterior Distributions'. Together they form a unique ...
A large-sample approximate multivariate normal Bayesian posterior distribution can be fruitfully used to guide either a traditional threshold proximity ...
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In probabilistic sensitivity analyses, analysts assign probability distributions to uncertain model parameters and use Monte Carlo simulation to estimate ...
Missing: Parametric | Show results with:Parametric
In probabilistic sensitivity analyses, analysts assign probability distributions to uncertain model parameters and use Monte Carlo simulation to estimate ...
Missing: Parametric | Show results with:Parametric
In probabilistic sensitivity analyses, analysts assign probability distributions to uncertain model parameters and use Monte Carlo simulation to estimate ...
Missing: Parametric | Show results with:Parametric
In probabilistic sensitivity analyses, analysts assign probability distributions to uncertain model parame- ters and use Monte Carlo simulation to estimate ...
Missing: Parametric | Show results with:Parametric
Abstract. We investigate approximate Bayesian inference techniques for nonlinear systems described by ordinary differential equation (ODE) models.
Missing: Parametric | Show results with:Parametric
Oct 31, 2018 · This paper develops tools for global prior sensitivity analysis in large Bayesian models. Without imposing parametric restrictions, ...