Unbiased parameter estimation of the Neyman-Scott model for rainfall simulation with related confidence interval
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Recent developments in discrete choice modelling have enabled the specification of models that can accomodate inter-alternative correlation and inter-agent taste heterogeneity. Nevertheless, to date, these two phenomena tend to have been addressed independ ...
This tutorial examines simple physical models of vehicle dynamics and overviews methods for parameter estimation and control. Firstly, techniques for the estimation of parameters that deal with constraints are detailed. Secondly, methods for controlling th ...
An analysis of the variance of the parameters of a multi-input plant estimated in closed-loop operation is performed. More specifically, the effect of the simultaneous excitation of an additional input on the variance of the estimated parameters is investi ...
One of the ongoing challenges in single particle fluorescence microscopy resides in estimating the axial position of particles with sub-resolution precision. Due to the complexity of the diffraction patterns generated by such particles, the standard fittin ...
A review; time and length scales accessible to ab initio mol. dynamics simulations are necessarily limited. This is the price one pays for an "in principle" unbiased description of chem. reactivity. We address the problem of times scales focusing on the de ...
Recent developments in discrete choice modelling have enabled the specification of models that can accomodate inter-alternative correlation and inter-agent taste heterogeneity. Nevertheless, to date, these two phenomena tend to have been addressed independ ...
An application to antenna optimization of bayesian network density of probability estimators is presented. This technique is very usefull for optimizations where abig number of parameters, multiple solutions and local minima increase the likelihood to conv ...
An application to antenna optimization of bayesian network density of probability estimators is presented. This technique is very usefull for optimizations where abig number of parameters, multiple solutions and local minima increase the likelihood to conv ...
A non-parametric method of distribution estimation for univariate data is presented. The idea is to adapt the smoothing spline procedure used in regression to the estimation of distributions via a scatterplot smoothing of theempirical distribution function ...
The joint problem of the recursive estimation of an optimal predictor for the closed-loop system and the unbiased parameter estimation of an ARMAX plant model in closed-loop operation is considered. A special reparameterized optimal predictor for the close ...