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Now showing items 13-32 of 40

  • 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 ...
  • Deletion diagnostics for the linear mixed model 

    Dillane, Dominic Mark (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2006)
    Modeling data is an integral element of modern statistical analysis. Methodological developments combined with the explosion in computing power over the past ten to fifteen years have greatly enhanced statisticians' ability ...
  • 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 ...
  • 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 ...
  • Fast approximate inverse Bayesian inference in non-parametric multivariate regression with application to palaeoclimate reconstruction 

    Salter-Townshend, Michael (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2009)
    Bayesian statistical methods often involve computationally intensive inference procedures. Sampling algorithms represent the current standard for fitting and testing models. Such methods, while flexible, are computationally ...
  • Fast sequential parameter inference for dynamic state space models 

    Bhattacharya, Arnab (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2012)
    Many problems in science require estimation and inference on systems that generate data over time. Such systems, quite common in statistical signal processing, time series analysis and econometrics, can be stated in a ...
  • Female entrepreneurship : an exploratory study of women entrepreneurs in Ireland 

    Humbert, Anne Laure (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2007)
    This thesis consists of an exploratory study of female entrepreneurship in Ireland, focusing on the motivations, obstacles and work/life balance experiences of entrepreneurs. The research relies on a combination of ...
  • 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 ...
  • 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 ...
  • Importance resampling MCMC : a methodology for cross-validation in inverse problems and its applications in model assessment 

    Bhattacharya, Sourabh (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2005)
    This thesis presents a methodology for implementing cross-validation in the context of Bayesian modelling of situations we loosely refer to as 'inverse problems'. It is motivated by an example from palaeoclimatology in ...
  • Improving exploration of posterior distributions in spatial models - a Markov chain Monte Carlo approach 

    Hayes, Bridette Anne-Marie (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2006)
    A Markov chain Monte Carlo (MCMC) algorithm is proposed for the evaluation of a posterior distribution. The posterior distribution is from a model that has a spatial structure and exhibits many characterisics which are ...
  • 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, ...
  • 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 ...
  • 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 ...
  • 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 ...
  • 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 ...
  • 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. ...
  • 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 ...
  • Spatial modelling of damage accumulation in bone cement 

    Heron, Elizabeth A. (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2005)
    In this thesis we develop spatial models for damage accumulation in the bone cement of hip replacement specimens. A total hip replacement consists of an artificial cup, forming the socket portion of the joint, and a ...
  • 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 ...