Sökning: "state space models"

Visar resultat 21 - 25 av 273 avhandlingar innehållade orden state space models.

  1. 21. State space representation for verification of open systems

    Författare :Irem Aktug; Mads Dam; Parosh Abdulla; KTH; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Computer science; Datalogi;

    Sammanfattning : When designing an open system, there might be no implementation available for cer- tain components at verification time. For such systems, verification has to be based on assumptions on the underspecified components. In this thesis, we present a framework for the verification of open systems through explicit state space representation. LÄS MER

  3. 22. Exploring the transcriptional space

    Författare :Joseph Bergenstråhle; Joakim Lundeberg; Jay W. Shin; KTH; []
    Nyckelord :Transcriptomics Spatial; Bioteknologi; Biotechnology;

    Sammanfattning : Transcriptomics promises biological insight into gene regulation, cell diversity, and mechanistic understanding of dysfunction. Driven by technological advancements in sequencing technologies, the field has witnessed an exponential growth in data output. Not only has the amount of raw data increased tremendously but it’s granularity as well. LÄS MER

  4. 23. Statistical inference with deep latent variable models

    Författare :Najmeh Abiri; Beräkningsbiologi och biologisk fysik - Genomgår omorganisation; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Deep Learning; Generative Models; Variational Inference; Missing data; Imputation; Fysicumarkivet A:2019:Abiri;

    Sammanfattning : Finding a suitable way to represent information in a dataset is one of the fundamental problems in Artificial Intelligence. With limited labeled information, unsupervised learning algorithms help to discover useful representations. LÄS MER

  5. 24. On particle-based online smoothing and parameter inference in general state-space models

    Författare :Johan Westerborn; Jimmy Olsson; Sumeetpal Singh; KTH; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Tillämpad matematik och beräkningsmatematik; Applied and Computational Mathematics;

    Sammanfattning : This thesis consists of 4 papers, presented in Paper A-D, on particle- based online smoothing and parameter inference in general state-space hidden Markov models.In Paper A a novel algorithm, the particle-based, rapid incremental smoother (PaRIS), aimed at efficiently performing online approxima- tion of smoothed expectations of additive state functionals in general hidden Markov models, is presented. LÄS MER

  7. 25. Seasonal Adjustment and Dynamic Linear Models

    Författare :Can Tongur; Daniel Thorburn; Sune Karlsson; Stockholms universitet; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Dynamic linear models; DLM; direct and indirect seasonal adjustment; relative efficiency; Huber loss function; Polls of polls; Wiener process; Swedish elections; Statistics; statistik;

    Sammanfattning : Dynamic Linear Models are a state space model framework based on the Kalman filter. We use this framework to do seasonal adjustments of empirical and artificial data. A simple model and an extended model based on Gibbs sampling are used and the results are compared with the results of a standard seasonal adjustment method. LÄS MER