Search for dissertations about: "model selection"

Showing result 1 - 5 of 845 swedish dissertations containing the words model selection.

  1. 1. Model Selection

    Author : Yngve Selén; Peter Stoica; Uppsala universitet; []
    Keywords : ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; 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

    Author : Yngve Selén; Peter Stoica; Jean-Jacques Fuchs; Uppsala universitet; []
    Keywords : ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; 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; Signal processing; 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

    Author : Torbjörn E. M. Nordling; Elling W Jacobsen; Rolf Findeisen; KTH; []
    Keywords : ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURVETENSKAP; NATURVETENSKAP; NATURVETENSKAP; TEKNIK OCH TEKNOLOGIER; NATURAL SCIENCES; NATURAL SCIENCES; NATURAL SCIENCES; ENGINEERING AND TECHNOLOGY; 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

    Author : Martin Eklund; Jarl Wikberg; Ron Wehrens; Uppsala universitet; []
    Keywords : NATURAL SCIENCES; NATURVETENSKAP; NATURVETENSKAP; NATURAL SCIENCES; bioinformatics; high-throughout biology; eScience; model selection; model assessment; Bioinformatics; 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. Covariate Model Building in Nonlinear Mixed Effects Models

    Author : Jakob Ribbing; E. Niclas Johnsson; Mats O. Karlsson; Janet Wade; Uppsala universitet; []
    Keywords : Pharmacokinetics Pharmacotherapy; Pharmacokinetics; Pharmacodynamics; Modeling; Covariate selection; Stepwise selection; Covariate analysis; Methodology; Model validation; Model evaluation; Type-2 diabetes; Beta-cell function; Meta analysis; Cross-validation; Least absolute shrinkage and selection operator; Pharmacometrics; ED optimization; Farmakokinetik Farmakoterapi;

    Abstract : Population pharmacokinetic-pharmacodynamic (PK-PD) models can be fitted using nonlinear mixed effects modelling (NONMEM). This is an efficient way of learning about drugs and diseases from data collected in clinical trials. READ MORE