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  • Bayesian modelling of short fatigue crack growth and coalescence 

    Walsh, Cathal Dominic (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2000)
    Failure of metal structures is caused by cracks appearing and growing in the material until the strength of the structure is compromised. The way in which such cracks grow in metal has been researched extensively; the great ...
  • A diagnostic for the general linear model : an application to Time Series 

    Sullivan, Carl (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2002)
    An outlier is an observation which is thought to be unusual. The detection of such extreme values is an important issue. Developing a model based on data containing even a single outlier can seriously bias population ...
  • 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 ...
  • 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 ...
  • The development of theory to assist the application of destination yield management 

    Mulvey, Michael F. (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2005)
    Destinations are geographical spaces where tourism experiences take place. They include the built and natural environm ent, attractions, the host community and commercial interests - predominantly SMEs. The role of ...
  • Bayesian approaches to content-based image retrieval 

    Stefanou, Georgios Andrea (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2006)
    This thesis addresses some issues in the relatively new field of Content-Based Image Retrieval. Content-based image retrieval is a technique that uses the visual content of images to aid searches from large scale image ...
  • 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 ...
  • 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 ...
  • Univariate time series modelling and forecasting using TSMARS 

    Keogh, Gerard (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2006)
    This thesis studies threshold nonlinearity in time series using TSMARS, a time series extension of the Multivariate Adaptive Regression Splines (MARS) procedure of Friedman (1991a). MARS is model free and can detect and ...
  • 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 ...
  • 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. ...
  • Utilisation of electronic fare collection data of urban bus operators with regard to transfer journeys and origin / destination estimation 

    Hofmann, Markus (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2008)
    The understanding of an urban public transport network from an operational point of view and the understanding of passenger’s travel patterns become increasingly important due to the growing complexity of most networks. ...
  • Bayesian kernel classification for high dimensional data with variable selection 

    Domijan, Katarina (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2009)
    High dimensional data sets, where the dimension of the measurements exceeds the number of samples, arise in many application domains. In particular, the development of genomic and proteomic technologies in the last decade ...
  • Statistical models for food authenticity 

    Toher, Deirdre Ann (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2009)
    The authentication of food samples pose a particular problem for regulators. The routine testing of premium food products, most likely to be subject to manipulation for commercial gain, is only feasible if the testing ...
  • 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 ...
  • 3D object reconstruction using multiple views 

    Kim, Donghoon (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2011)
    3D object modelling from multiple view images has recently been of increasing interest in computer vision. Two techniques, Visual Hull and Photo Hull, have been extensively studied in the hope of developing 3D shape ...
  • Variational Bayes approximation for inverse regression problems 

    Vatsa, Richa (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2011)
    Inverse regression is a tool to predict an unknown explanatory variable for given observations of a response variable in a regression problem. The prediction problem is usually carried out in two stages: firstly, to fit ...
  • A new method to implement Bayesian inference on stochastic differential equation models 

    Joshi, Chaitanya (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2011)
    Stochastic differential equations (SDEs) are widely used to model numerous real-life phenomena. However, transition densities of most of the SDE models used in practice are not known, making both likelihood based and ...
  • 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 ...
  • 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 ...