Sökning: "Blandningsmodeller"

Hittade 4 uppsatser innehållade ordet Blandningsmodeller.

  1. 1. Assessment of Modern Statistical Modelling Methods for the Association of High-Energy Neutrinos to Astrophysical Sources

    Master-uppsats, KTH/Matematisk statistik

    Författare :Valentin Minoz; [2021]
    Nyckelord :Statistics; Astrophysics; Neutrino sources; Mixture models; Monte Carlo methods; Maximum likelihood estimation; Statistik; Astrofysik; Neutrinokällor; Blandningsmodeller; Monte Carlo-metoder; Maximum likelihood-metoden;

    Sammanfattning : The search for the sources of astrophysical neutrinos is a central open question in particle astrophysics. Thanks to substantial experimental efforts, we now have large-scale neutrino detectors in the oceans and polar ice. LÄS MER

  2. 2. Normalizing Flow based Hidden Markov Models for Phone Recognition

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

    Författare :Anubhab Ghosh; [2020]
    Nyckelord :Phone recognition; generative learning; Normalizing flows; Decision fusion; Speech recognition;

    Sammanfattning : The task of Phone recognition is a fundamental task in Speech recognition and often serves a critical role in bench-marking purposes. Researchers have used a variety of models used in the past to address this task, using both generative and discriminative learning approaches. LÄS MER

  3. 3. Customer segmentation of retail chain customers using cluster analysis

    Master-uppsats, KTH/Matematisk statistik

    Författare :Sebastian Bergström; [2019]
    Nyckelord :Cluster analysis; customer segmentation; tEIGEN; MCLUST; K-means; NMF; Silhouette; Davies-Bouldin; big spenders; statistics; applied mathematics; unsupervised learning; Klusteranalys; kundsegmentering; tEIGEN; MCLUST; K-means; NMF; Silhouette; Davies-Bouldin; storkonsumenter; statistik; tillämpad matematik;

    Sammanfattning : In this thesis, cluster analysis was applied to data comprising of customer spending habits at a retail chain in order to perform customer segmentation. The method used was a two-step cluster procedure in which the first step consisted of feature engineering, a square root transformation of the data in order to handle big spenders in the data set and finally principal component analysis in order to reduce the dimensionality of the data set. LÄS MER

  4. 4. Towards disease progression sub-typing via responsibility sampling for robust expectation-maximisation learning

    Master-uppsats, KTH/Optimeringslära och systemteori

    Författare :Mathias Edman; [2019]
    Nyckelord :;

    Sammanfattning : Most diseases have different heterogeneous effects on patients. Broadly, one may conclude what manifested symptoms correspond to which diagnosis, but usually there is more than one disease progression pattern. LÄS MER