Sökning: "sequential Monte Carlo methods"

Visar resultat 1 - 5 av 13 uppsatser innehållade orden sequential Monte Carlo methods.

  1. 1. Longitudinal Group Sequential Testing

    Magister-uppsats, Uppsala universitet/Statistiska institutionen

    Författare :Emil Lanzén; [2023]
    Nyckelord :;

    Sammanfattning : Sequential testing is used in clinical trials and online experiments to terminate trials early if there is sufficient evidence of a treatment effect, reducing the subjects’ exposure to potentially harmful treatments. However, incomplete follow-up of trial subjects can lead to biased estimation of the treatment effect. LÄS MER

  3. 2. KL/TV Reshuffling : Statistical Distance Based Offspring Selection in SMC Methods

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Oskar Kviman; [2022]
    Nyckelord :;

    Sammanfattning : Over the years sequential Monte Carlo (SMC), and, equivalently, particle filter (PF) theory has enjoyed much attention from researchers. However, the intensity of developing innovative resampling methods, also known as offspring selection methods, has long been declining, with most of the popular schemes aging back two decades. LÄS MER

  4. 3. Applying Model Selection on Ligand-Target Binding Kinetic Analysis

    Master-uppsats, KTH/Proteinvetenskap

    Författare :Klara Djurberg; [2021]
    Nyckelord :LigandTracer; kinetic models; Rituximab; Bayesian inference; Bayesian model selection; LigandTracer; interaktionsmodeller; Rituximab; Bayesiansk inferens; modellval;

    Sammanfattning : The time-course of interaction formation or breaking can be studied using LigandTracer, and the data obtained from an experiment can be analyzed using a model of ligand-target binding kinetics. There are different kinetic models, and the choice of model is currently motivated by knowledge about the interaction, which is problematic when the knowledge about the interaction is unsatisfactory. LÄS MER

  5. 4. Particle Filter Bridge Interpolation in GANs

    Master-uppsats, KTH/Matematisk statistik

    Författare :Viktor Käll; Erik Piscator; [2021]
    Nyckelord :Generative modeling; Generative adversarial network; Convolutional neural network; Stochastic interpolation; Gaussian process; Gaussian bridge process; Sequential Monte Carlo; Particle filter; Generativ modellering; Generative adversarial network; Neuralt faltningsnätverk; Stokastisk interpolation; Gaussisk process; Gaussisk bryggprocess; Sekventiell Monte Carlo; Partikelfilter;

    Sammanfattning : Generative adversarial networks (GANs), a type of generative modeling framework, has received much attention in the past few years since they were discovered for their capacity to recover complex high-dimensional data distributions. These provide a compressed representation of the data where all but the essential features of a sample is extracted, subsequently inducing a similarity measure on the space of data. LÄS MER

  7. 5. Particle-Based Online Bayesian Learning of Static Parameters with Application to Mixture Models

    Master-uppsats, KTH/Matematisk statistik

    Författare :Rutger Fuglesang; [2020]
    Nyckelord :Statistics; applied mathematics; sequential monte carlo; SMC; Statistical inference; Statisk; sekventiell monte carlo; SMC; tillämpad matematik;

    Sammanfattning : This thesis investigates the possibility of using Sequential Monte Carlo methods (SMC) to create an online algorithm to infer properties from a dataset, such as unknown model parameters. Statistical inference from data streams tends to be difficult, and this is particularly the case for parametric models, which will be the focus of this paper. LÄS MER