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  • Statistical framework for multi sensor fusion and 3D reconstruction 

    Ruttle, Jonathan (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2012)
    Multi-view 3D reconstruction is an area of computer vision where multiple images are taken of an object and information in those images is used to generate a 3D model describing the shape and size of that object. The ...
  • MCMC for inference on phase-type and masked system lifetime models 

    Aslett, Louis J.M. (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2012)
    Common reliability data consist of lifetimes (of censoring information) on all components and systems under examination. However, masked system lifetime data represents an important class of problems where the information ...
  • 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 ...
  • 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 ...
  • 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 ...
  • 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. ...
  • Competing risks of default and prepayment of mortgage market 

    OLAJUBU, OLUWATOBILOBA JOHN JOHN (Trinity College Dublin. School of Computer Science & Statistics. Discipline of Statistics, 2020)
    Using a large data set on the Single family home loans from The Federal Home Loan Mortgage Corporation (FHLMC), sponsored by the US government, this research studies the economic factors affecting the competing risks of ...
  • Efficient and scalable inference for generalized student - T process models 

    ROETZER, GERNOT RUDOLF (Trinity College Dublin. School of Computer Science & Statistics. Discipline of Statistics, 2020)
    Gaussian Processes are a popular, nonparametric modelling framework for solving a wide range of regression problems. However, they are suffering from 2 major shortcomings. On the one hand, they require efficient, approximate ...
  • Image Restoration Using Deep Learning 

    ALBLUWI, FATMA HAMED (Trinity College Dublin. School of Computer Science & Statistics. Discipline of Statistics, 2020)
    In this thesis, we propose several convolutional neural network (CNN) architectures with fewer parameters compared to state-of-the-art deep structures to restore original images from degraded versions. Employing fewer ...
  • Matching-adjusted indirect comparisons: identifying method variations and implementing models in R 

    CASSIDY, OWEN CHRISTOPHER (Trinity College Dublin. School of Computer Science & Statistics. Discipline of Statistics, 2020)
    In the framework of evidence-based medicine, comparative effectiveness research is a fundamental activity to the development of pharmaceutical products and medical treatments. For a given medical condition, several competing ...
  • Modelling the distribution of grouped survival data via dependant neutral-to-the-right priors 

    DONAGHY, FEARGHAL (Trinity College Dublin. School of Computer Science & Statistics. Discipline of Statistics, 2020)
    With each update of its browser, Firefox receives reports of the time of discovery of a large number of bugs associated with that update. This process yields survival data which is separated by update into groups and often ...
  • An Integrated Framework for Estimating the Number of Classes with Application for Species Estimation 

    Al-Ghamdi, Asmaa (Trinity College Dublin. School of Computer Science & Statistics. Discipline of Statistics, 2021)
    The two most common approaches for estimating the number of distinct classes within a population are either to use sampling data directly with combinatorial arguments or to extrapolate historical discovery data. However, ...
  • Effect of plant diversity and drought on the agronomic performance of intensively managed grassland communities 

    Grange, Guylain (Trinity College Dublin. School of Computer Science & Statistics. Discipline of Statistics, 2022)
    Temperate agro-ecosystems are crucial for food production and financially important for the rural economy, but can have strong environmental impacts and are threatened by increased frequency of extreme weather events. Over ...
  • Consistent Mode-Finding for Parametric and Non-Parametric Clustering 

    Tobin, Joshua (Trinity College Dublin. School of Computer Science & Statistics. Discipline of Statistics, 2022)
    Density peaks clustering detects modes as points with high density and large distance to points of higher density. To cluster the observed samples, points are assigned to the same cluster as their nearest neighbor of higher ...