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  • 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. ...

  • Topics in unsupervised learning 

    McNicholas, Paul David (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2007)
    Two topics in unsupervised learning are reviewed and developed; namely, model-based clustering and association rule mining. A new family of Gaussian mixture models, with a parsim onious covariance structure, is introduced. ...

  • 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 ...

  • 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 ...

  • 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 ...

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