981 resultados para adaptive estimation


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Reaction-diffusion problems, finite elements, unstructured grid, grid adaption, W-method, stiffness, local partitioning, excitable medium, spiral wave drift

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Handschrifterkennung, Wortsegmentierung, strukturelle Ziffernerkennung, strukturelle Worterkennung, Anpassung, Anpassbarkeit, historische Dokumente, Kirchenbücher, robuste Worterkennung

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Magdeburg, Univ., Fak. für Informatik, Diss., 2007

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Fluidized beds, granulation, heat and mass transfer, calcium dynamics, stochastic process, finite element methods, Rosenbrock methods, multigrid methods, parallelization

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Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2007

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Contact Tasks of Robotic Systems, Mobile Legged Robots, Industrial Manipulators, Service Operations, Robot's Locomotion, Adaptive Control, Impedance/Force Control

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Magdeburg, Univ., Fak. für Informatik, Diss., 2010

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Magdeburg, Univ., Fak. für Informatik, Habil.-Schr., 2010

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This work describes a test tool that allows to make performance tests of different end-to-end available bandwidth estimation algorithms along with their different implementations. The goal of such tests is to find the best-performing algorithm and its implementation and use it in congestion control mechanism for high-performance reliable transport protocols. The main idea of this paper is to describe the options which provide available bandwidth estimation mechanism for highspeed data transport protocols and to develop basic functionality of such test tool with which it will be possible to manage entities of test application on all involved testing hosts, aided by some middleware.

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Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2015

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Otto-von-Guericke-Universität Magdeburg, Fakultät für Mathematik, Dissertation, 2016

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This comment corrects the errors in the estimation process that appear in Martins (2001). The first error is in the parametric probit estimation, as the previously presented results do not maximize the log-likelihood function. In the global maximum more variables become significant. As for the semiparametric estimation method, the kernel function used in Martins (2001) can take on both positive and negative values, which implies that the participation probability estimates may be outside the interval [0,1]. We have solved the problem by applying local smoothing in the kernel estimation, as suggested by Klein and Spady (1993).

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Given a model that can be simulated, conditional moments at a trial parameter value can be calculated with high accuracy by applying kernel smoothing methods to a long simulation. With such conditional moments in hand, standard method of moments techniques can be used to estimate the parameter. Since conditional moments are calculated using kernel smoothing rather than simple averaging, it is not necessary that the model be simulable subject to the conditioning information that is used to define the moment conditions. For this reason, the proposed estimator is applicable to general dynamic latent variable models. Monte Carlo results show that the estimator performs well in comparison to other estimators that have been proposed for estimation of general DLV models.

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Restriction site-associated DNA sequencing (RADseq) provides researchers with the ability to record genetic polymorphism across thousands of loci for nonmodel organisms, potentially revolutionizing the field of molecular ecology. However, as with other genotyping methods, RADseq is prone to a number of sources of error that may have consequential effects for population genetic inferences, and these have received only limited attention in terms of the estimation and reporting of genotyping error rates. Here we use individual sample replicates, under the expectation of identical genotypes, to quantify genotyping error in the absence of a reference genome. We then use sample replicates to (i) optimize de novo assembly parameters within the program Stacks, by minimizing error and maximizing the retrieval of informative loci; and (ii) quantify error rates for loci, alleles and single-nucleotide polymorphisms. As an empirical example, we use a double-digest RAD data set of a nonmodel plant species, Berberis alpina, collected from high-altitude mountains in Mexico.