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Recent Submissions
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Challenges and Adaptations of Model-Based Clustering for Flow and Mass Cytometry
(2025)Model-based clustering is a statistical approach to cluster analysis, which has been successfully deployed in a number of domains due to its principled framework, clear assumptions, and adaptability. For these reasons, ... -
Theoretical developments of modelling techniques and novel visualisations for studying the biodiversity and ecosystem function relationship; and their application for calculating the economic value of biodiversity.
(Trinity College Dublin. School of Computer Science & Statistics. Discipline of Statistics, 2025)The biodiversity and ecosystem function (BEF) relationship studies how species diversity within an ecosystem affects the outputs (called ecosystem functions) the system produces. In recent decades, there has been great ... -
Design and Analysis of Biodiversity Experiments
(Trinity College Dublin. School of Computer Science & Statistics. Discipline of Statistics, 2025)Biodiversity and ecosystem functioning (BEF) relationships define the ways in which the diversity of species in an ecosystem drive the quantity and quality of the goods and services provided. Species diversity may be defined ... -
A Clustering Framework for Functional Data: Functional Gaussian Process Mixture Model (FunGP)
(Trinity College Dublin. School of Computer Science & Statistics. Discipline of Statistics, 2024)Functional data analysis (FDA) is a rapidly evolving field that focuses on the analysis and interpretation of data where each observation is a function, typically represented by curves or surfaces over a continuum such as ... -
Parallelizing Adaptive Reliability Analysis through Penalizing the Learning Function
(2024)Structural reliability analysis is essential for evaluating system failure probabilities under uncertainties, yet it often faces computational efficiency challenges. While surrogate model based techniques, including ... -
Learning Mixtures of Gaussian Processes through Random Projection
(2024)We propose an ensemble clustering framework to uncover latent cluster labels in functional data generated from a Gaussian process mixture. Our method exploits the fact that the projection coefficients of the functional ... -
Statistical Methods to Extrapolate Time-To-Event Data
(Trinity College Dublin. School of Computer Science & Statistics. Discipline of Statistics, 2024)This thesis investigates methods used to predict long-term survival of observations (typically survival times) beyond the time at which data follow-up is available. Current practice is to use parametric survival models; ... -
Bayesian Tree Regression within a Streaming Context
(Trinity College Dublin. School of Computer Science & Statistics. Discipline of Statistics, 2023)Regression in a statistical streaming environment. Explore either large amounts of data or data that is continually being generated in a meaningful way. The streaming setting is challenging because either the proportion ... -
A Theoretical Analysis of Density Peaks Clustering and the Component-wise Peak-Finding Algorithm
(2024)Density peaks clustering detects modes as points with high density and large distance to points of higher density. Each non-mode point is assigned to the same cluster as its nearest neighbor of higher density. Density ... -
Distributed Lag Regression Methods and Compartmental Models for Analysis of Disease Progression
(Trinity College Dublin. School of Computer Science & Statistics. Discipline of Statistics, 2023)ANCA vasculitis is an autoimmune disease characterised by relapses, or flares, that can have a severe detrimental impact on patient health. Flares can be prevented by suppressing the immune system but this exposes the ... -
Incorporating Ignorance within Game Theory: An Imprecise Probability Approach
(Trinity College Dublin. School of Computer Science & Statistics. Discipline of Statistics, 2023)Ignorance within non-cooperative games, reflected as a player's uncertain preferences towards a game's outcome, is examined from a probabilistic point of view. This topic has had scarce treatment in the literature, which ... -
Reinforced EM Algorithm for Clustering with Gaussian Mixture Models
(2023)Methods that employ the EM algorithm for parameter estimation typically face the notorious yet unsolved problem that the initialization input significantly impacts the algorithm output. We here develop a Reinforced ... -
Incorporating Ignorance within Game Theory: An Imprecise Probability Approach
(2023)Ignorance within non-cooperative games, reflected as a player’s uncertain prefer- ences towards a game’s outcome, is examined from a Bayesian point of view. This topic has had scarce treatment in the literature, which ... -
Consistent Mode-Finding for Parametric and Non-Parametric Clustering
(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 ... -
Effect of plant diversity and drought on the agronomic performance of intensively managed grassland communities
(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 ... -
Saddlepoint Approximation for the Generalized Inverse Gaussian Levy Process
(2022)The generalized inverse Gaussian (GIG) Lévy process is a limit of compound Poisson processes, including the stationary gamma process and the stationary inverse Gaussian process as special cases. However, fitting the GIG ... -
DCF: An Efficient and Robust Density-Based Clustering Method
(2021)Density-based clustering methods have been shown to achieve promising results in modern data mining applications. A recent approach, Density Peaks Clustering (DPC), detects modes as points with high density and large ... -
Semantic image segmentation based on spatial relationships and inexact graph matching
(2020)We propose a method for semantic image segmentation, combining a deep neural network and spatial relationships between image regions, encoded in a graph representation of the scene. Our proposal is based on inexact graph ... -
An Integrated Framework for Estimating the Number of Classes with Application for Species Estimation
(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, ...
