Sort by: Order: Results:

Now showing items 21-30 of 30

  • Bayesian inference for short term traffic forecasting 

    Mai, Tiep K. (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2013)
    In intelligent transport systems, short term traffic forecasting is one of the most important problems, reflecting the network state in the near future and feeding information to other application modules. Even though ...
  • An exploratory study of gender segregation in investment management in Ireland 

    Sheerin, Corina (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2013)
    Despite the entry of women in recent years, Investment Management remains a male domain. The absence of women is most notable in the fund management suite and on the trading floor (the most lucrative sub sectors of the ...
  • L_ Inference for shape parameter estimation 

    Arellano Vidal, Claudia L. (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2014)
    In this thesis, we propose a method to robustly estimate the parameters that controls the mapping of a shape (model shape) onto another (target shape). The shapes of interest are contours in the 2D space, surfaces in the ...
  • Visual attention using 2D & 3D displays 

    Zdziarski, Zbigniew (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2015)
    In the past three decades, robotists and computer vision scientists, inspired by psychological and neurophysiological studies, have developed many computational models of attentions (CMAs) that mimic the behaviour of the ...
  • Tracking the distribution of bugs across software release versions 

    Ó Ríordáin, Seán (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2015)
    Real software systems always contain bugs and the question on every release manager’s mind coming up to a release centres around how many undiscovered bugs there still remain. This work looks at one model, (Goel and ...
  • Bayesian inference for misaligned irregular time series with application to palaeoclimate reconstruction 

    Doan, Thinh K. (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2015)
    This thesis proposes new Bayesian methods to jointly analyse misaligned irregular time series. Temporal misalignment occurs wdien multiple irregularly spaced time series are considered together, or when the time periods ...
  • Reliability updating in linear opinion pooling for multiple decision makers 

    Bolger, Donnacha (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2016)
    Accurate information sources are vital prerequisites for good decision making. In this thesis we consider a multiple participant setting, where all decision makers (DMs) have a collection of neighbours with whom they share ...
  • A risk assessment tool for highly energetic break-up events during the atmospheric re-entry 

    De Persis, Cristina (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2017)
    Most unmanned space missions end up with a destructive atmospheric re-entry. From ten to forty percent of a re-entering satellite’s mass may survive re-entry and hit the Earth’s surface. This has the potential to be a ...
  • The Impact of Performing a Network Meta-Analysis with Imperfect Evidence 

    LEAHY, JOY (Trinity College Dublin. School of Computer Science & Statistics. Discipline of Statistics, 2019)
    Network meta-analysis (NMA) is an important aspect of evidence synthesis in a clinical setting, as it allows us to compare treatments which may not have been analysed in the same trial. In an ideal scenario we would have ...
  • Modelling Uncertainty and Vagueness within Recommender Systems via Nonparametric Predictive Inference 

    MCCOURT, ANGELA (Trinity College Dublin. School of Computer Science & Statistics. Discipline of Statistics, 2019)
    The way in which we learn is the subject of considerable research within multiple disciplines. There is also a vast amount of on-line material available to us, causing decision-making to become increasingly difficult. ...