Search for dissertations about: "Model selection"

Showing result 1 - 5 of 665 swedish dissertations containing the words Model selection.

  1. 1. Model Selection

    University dissertation from Uppsala University

    Author : Yngve Selén; Uppsala universitet.; Uppsala universitet.; [2004]
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Electrical Engineering with specialization in Signal Processing; Elektroteknik med inriktning mot signalbehandling;

    Abstract : Before using a parametric model one has to be sure that it offers a reasonable description of the system to be modeled. If a bad model structure is employed, the obtained model will also be bad, no matter how good is the parameter estimation method. There exist many possible ways of validating candidate models. READ MORE

  2. 2. Model Selection and Sparse Modeling

    University dissertation from Uppsala : Institutionen för informationsteknologi

    Author : Yngve Selén; Uppsala universitet.; Uppsala universitet.; [2007]
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; model selection; model order selection; model averaging; nested models; sparse models; Bayesian inference; MMSE estimation; MAP estimation; ML estimation; AIC; BIC; GIC; RAKE receivers; pulse compression; radar; linear models; linear regression models; TECHNOLOGY Information technology Signal processing; TEKNIKVETENSKAP Informationsteknik Signalbehandling;

    Abstract : Parametric signal models are used in a multitude of signal processing applications. This thesis deals with signals for which there are many candidate models, and it is not a priori known which model is the most appropriate one. READ MORE

  3. 3. Robust inference of gene regulatory networks System properties, variable selection, subnetworks, and design of experiments

    University dissertation from Stockholm : KTH Royal Institute of Technology

    Author : Torbjörn E. M. Nordling; KTH.; [2013]
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; network inference; reverse engineering; variable selection; model selection; feature selection; subset selection; system identification; system theory; network theory; subnetworks; design of experiments; perturbation experiments; gene regulatory networks; biological networks;

    Abstract : In this thesis, inference of biological networks from in vivo data generated by perturbation experiments is considered, i.e. deduction of causal interactions that exist among the observed variables. Knowledge of such regulatory influences is essential in biology. READ MORE

  4. 4. eScience Approaches to Model Selection and Assessment Applications in Bioinformatics

    University dissertation from Uppsala : Acta Universitatis Upsaliensis

    Author : Martin Eklund; Uppsala universitet.; [2009]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; bioinformatics; high-throughout biology; eScience; model selection; model assessment; TECHNOLOGY Bioengineering Bioinformatics; TEKNIKVETENSKAP Bioteknik Bioinformatik;

    Abstract : High-throughput experimental methods, such as DNA and protein microarrays, have become ubiquitous and indispensable tools in biology and biomedicine, and the number of high-throughput technologies is constantly increasing. They provide the power to measure thousands of properties of a biological system in a single experiment and have the potential to revolutionize our understanding of biology and medicine. READ MORE

  5. 5. Stochastic model updating and model selection with application to structural dynamics

    University dissertation from Chalmers University of Technology

    Author : Majid Khorsand Vakilzadeh; Chalmers tekniska högskola.; Chalmers University of Technology.; Chalmers tekniska högskola.; Chalmers University of Technology.; [2016]
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Uncertainty quantification; Bayesian model updating; Bayesian model selection; stochastic simulation; Bootstrapping; Subspace system identification; Finite element model;

    Abstract : Uncertainty induced by our incomplete state of knowledge about engineering systems and their surrounding environment give rise to challenging problems in the process of building predictive models for the system behavior. One such challenge is the model selection problem, which arises due to the existence of invariably multiple candidate models with different mathematical forms to represent the system behavior, and so there is a need to assess their plausibility based on experimental data. READ MORE