and Jeon, J.(2002), 'Estimation of input parameters in complex simulation using a Gaussian process metamodel', Probabilistic Eng. Mech., Vol. 17, pp. 219-225J.S.Park,,J.Jeon.Estimation of Input Parameters in Complex Simulation Using a Gaussian Process metamodel.Probabilistic Engineering ...
Complex parameters, such as poverty indicators, are usually difficult to predict in small area estimation (SAE). Elbers et al. (2003) have proposed an empirical semi-parametric method for dealing with poverty indices in SAE. This method, commonly called the ELL method, consists of drawing from...
Complete genotype information from populations allows increased resolution of parameters in complex evolutionary or demographic models. The challenge is to develop computational methods that permit the efficient use of such large-scale datasets. Likelihood-based coalescent methods have proven very flexible ...
Both parameter estimation methods manage to stay in the error bars, yet the BPE result has a far more physically realistic pair of parameters! This is the main purpose using PEUQSE in order to do BPE: it will tend to give more realistic parameter estimates, and can even give a type of ...
For implicitly created state-space models, you specify the model structure and the location of the unknown parameters using the parameter-to-matrix mapping function. Implicitly create a state-space model to estimate complex models, impose parameter constraints, and estimate initial states. The parameter...
Estimation of parameters in ordinary differential equations given discrete time measurement data is a complex problem, which has been addressed by several authors in many different fields. Given a model structure and discrete time measurement data we are interested in identifying the values of the para...
estimatefits regression coefficients along with all other state-space model parameters. The software is flexible enough to allow applying constraints to the regression coefficients using constrained optimization options. For more details, see theName,Valuepair arguments andfmincon. ...
AIC— Raw Akaike Information Criteria (AIC) measure of model quality AICc— Small-sample-size corrected AIC nAIC— Normalized AIC BIC— Bayesian Information Criteria (BIC) Parameters Estimated values of model parameters OptionsUsed Option set used for estimation. If no custom options were configured,...
Thus, Simplex cannot estimate parameters that are constrained to be integers (for example, the number of times people are assumed to rehearse an item before recalling it). - In those cases, one can perform Simplex estimation for each value of the integer parameter - Alternatively, the model ...
While it is extremely difficult to assess the usefulness of such constrained parameter estimation for the performance of complex models, the approach adds robustness to the estimated parameters at relatively low cost in terms of SWC fit. The concept of injecting physical constraints to SWC parameter ...