• 

  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 ...
• 

  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 ...
• 

  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 ...
• 

  Advances in Bayesian model development and inversion in multivariate inverse inference problems : with application to palaeoclimate reconstruction 

  Sweeney, James (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2012)

  An extremely challenging example of a multivariate inverse inference problem is the statistical reconstruction of palaeoclimate from fossil pollen data, which represents the motivating research problem considered in this ...
• 

  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 inyolye computationally intensive inference procedures. Sampling algorithms represent the current standard for fitting and testing models. Such methods, while flexible, are computationally ...

View more