Sökning: "Selection Models"

Visar resultat 1 - 5 av 635 avhandlingar innehållade orden Selection Models.

  1. 1. Model Selection and Sparse Modeling

    Författare :Yngve Selén; Peter Stoica; Jean-Jacques Fuchs; Uppsala universitet; []
    Nyckelord :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;

    Sammanfattning : 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. LÄS MER

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

    Författare :Torbjörn E. M. Nordling; Elling W Jacobsen; Rolf Findeisen; KTH; []
    Nyckelord :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;

    Sammanfattning : 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. LÄS MER

  4. 3. Model Selection

    Författare :Yngve Selén; Peter Stoica; Uppsala universitet; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Electrical Engineering with specialization in Signal Processing; Elektroteknik med inriktning mot signalbehandling;

    Sammanfattning : 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. LÄS MER

  5. 4. Models in Neutrino Physics : Numerical and Statistical Studies

    Författare :Johannes Bergström; Tommy Ohlsson; Pilar Hernandez; KTH; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Neutrino mass; lepton mixing; Majorana neutrinos; neutrino oscillations; neutrinoless double beta decay; statistical methods; Bayesian inference; model selection; effective field theory; Weinberg operator; seesaw models; inverse seesaw; right-handed neutrinos; renormalization group; threshold effects.; Neutrinomassor; leptonblandning; Majorananeutriner; neutrinooscillationer; neutrinol¨ost dubbelt betas¨onderfall; statistiska metoder; Bayesisk inferens; modellval; effektiv f¨altteori; Weinbergoperator; seesawmodeller; invers seesaw; högerhänta neutriner; renormeringsgrupp; tröskeleffekter.;

    Sammanfattning : The standard model of particle physics can excellently describe the vast majorityof data of particle physics experiments. However, in its simplest form, it cannot account for the fact that the neutrinos are massive particles and lepton flavorsmixed, as required by the observation of neutrino oscillations. LÄS MER

  7. 5. Regressor and Structure Selection : Uses of ANOVA in System Identification

    Författare :Ingela Lind; Lennart Ljung; Torsten Söderström; Linköpings universitet; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; System identification; Regressor selection; Analysis of variance; Nonlinear systems; Structure selection; Automatic control; Reglerteknik;

    Sammanfattning : Identification of nonlinear dynamical models of a black box nature involves both structure decisions (i.e., which regressors to use and the selection of a regressor function), and the estimation of the parameters involved. LÄS MER