43 citations · 93 across the 18 of their papers we have counts for
18 papers
A real-time metric of online engagement monitoring
Laura J. Johnston, Jim E. Griffin, Ioanna Manolopoulou +1
Measuring online behavioural student engagement often relies on simple count indicators or retrospective, predictive methods, which present challenges for real-time application. To…
Uncovering Student Engagement Patterns in Moodle with Interpretable Machine Learning
Laura J. Johnston, Jim E. Griffin, Ioanna Manolopoulou +1
Understanding and enhancing student engagement through digital platforms is critical in higher education. This study introduces a methodology for quantifying engagement across an e…
Bayesian Nonparametric Models for Multiple Raters: a General Statistical Framework
Giuseppe Mignemi, Ioanna Manolopoulou
Rating procedure is crucial in many applied fields (e.g., educational, clinical, emergency). It implies that a rater (e.g., teacher, doctor) rates a subject (e.g., student, doctor)…
Mixture polarization in inter-rater agreement analysis: a Bayesian nonparametric index
Giuseppe Mignemi, Antonio Calcagnì, Andrea Spoto +1
In several observational contexts where different raters evaluate a set of items, it is common to assume that all raters draw their scores from the same underlying distribution. Ho…
Counterfactual Learning with Multioutput Deep Kernels
Alberto Caron, Gianluca Baio, Ioanna Manolopoulou
In this paper, we address the challenge of performing counterfactual inference with observational data via Bayesian nonparametric regression adjustment, with a focus on high-dimens…
Interpretable Deep Causal Learning for Moderation Effects
Alberto Caron, Gianluca Baio, Ioanna Manolopoulou
In this extended abstract paper, we address the problem of interpretability and targeted regularization in causal machine learning models. In particular, we focus on the problem of…
Regional Topics in British Grocery Retail Transactions
Mariflor Vega Carrasco, Mirco Musolesi, Jason O'Sullivan +2
Understanding the customer behaviours behind transactional data has high commercial value in the grocery retail industry. Customers generate millions of transactions every day, cho…
Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation
Alberto Caron, Gianluca Baio, Ioanna Manolopoulou
This paper develops a sparsity-inducing version of Bayesian Causal Forests, a recently proposed nonparametric causal regression model that employs Bayesian Additive Regression Tree…
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a Review
Alberto Caron, Gianluca Baio, Ioanna Manolopoulou
Large observational data are increasingly available in disciplines such as health, economic and social sciences, where researchers are interested in causal questions rather than pr…
Modelling Grocery Retail Topic Distributions: Evaluation, Interpretability and Stability
Mariflor Vega-Carrasco, Jason O'sullivan, Rosie Prior +2
Understanding the shopping motivations behind market baskets has high commercial value in the grocery retail industry. Analyzing shopping transactions demands techniques that can c…
Incorporating Astrochemistry into Molecular Line Modelling via Emulation
Damien de Mijolla, Serena Viti, Jonathan Holdship +2
In studies of the interstellar medium in galaxies, radiative transfer models of molecular emission are useful for relating molecular line observations back to the physical conditio…
Bayesian hierarchical modelling of sparse count processes in retail analytics
James Pitkin, Ioanna Manolopoulou, Gordon Ross
The field of retail analytics has been transformed by the availability of rich data which can be used to perform tasks such as demand forecasting and inventory management. However,…
Dirichlet Process Mixtures of Order Statistics with Applications to Retail Analytics
James Pitkin, Gordon Ross, Ioanna Manolopoulou
The rise of "big data" has led to the frequent need to process and store datasets containing large numbers of high dimensional observations. Due to storage restrictions, these obse…
Estimating the Expected Value of Sample Information across Different Sample Sizes using Moment Matching and Non-Linear Regression
Anna Heath, Ioanna Manolopoulou, Gianluca Baio
Background: The Expected Value of Sample Information (EVSI) determines the economic value of any future study with a specific design aimed at reducing uncertainty in a health econo…
Efficient Monte Carlo Estimation of the Expected Value of Sample Information using Moment Matching
Anna Heath, Ioanna Manolopoulou, Gianluca Baio
The Expected Value of Sample Information (EVSI) is used to calculate the economic value of a new research strategy. While this value would be important to both researchers and fund…
BPEC: An R Package for Bayesian Phylogeographic and Ecological Clustering
Ioanna Manolopoulou, Axel Hille, Brent Emerson
BPEC is an R package for Bayesian Phylogeographic and Ecological Clustering which allows geographical, environmental and phenotypic measurements to be combined with DNA sequences i…
Estimating the Expected Value of Partial Perfect Information in Health Economic Evaluations using Integrated Nested Laplace Approximation
Anna Heath, Ioanna Manolopoulou, Gianluca Baio
The Expected Value of Perfect Partial Information (EVPPI) is a decision-theoretic measure of the "cost" of parametric uncertainty in decision making used principally in health econ…
A Bayesian partial identification approach to inferring the prevalence of accounting misconduct
P. Richard Hahn, Jared S. Murray, Ioanna Manolopoulou
This paper describes the use of flexible Bayesian regression models for estimating a partially identified probability function. Our approach permits efficient sensitivity analysis…