Andrew Parnell

Hamilton Professor

Maynooth University

Ireland

Research Interests

Explore related searches

Professors in Ireland Professors in Statistics Statistics professors in Ireland

Contact this professor

ORCID

Google Scholar

Academic Page

About

Andrew Parnell is a Hamilton Professor at Maynooth University in Ireland. His research interests span a range of areas including Bayesian modeling, ecological data analysis, and machine learning applications in environmental and food safety contexts. He has published recent articles on topics such as zero-and-N-inflated distributions, palaeoclimate reconstruction, and the impact of dietary variation on animal isotopes.

Andrew Parnell

Research Interests

Explore related searches

Contact this professor

About

Articles (20)

Finite mixture representations of zero-and-N-inflated distributions for count-compositional data

SCUBIDO: a Bayesian modelling approach to reconstruct palaeoclimate from multivariate lake sediment data

ExActR: A Shiny app for creating ecosystem extent accounts

Bivariate Extreme Value Analysis for Space Weather Risk Assessment: Solar Wind—Magnetosphere Driving in the Terrestrial System

Machine Learning Applied to the Detection of Mycotoxin in Food: A Systematic Review

How strongly does diet variation explain variation in isotope values of animal consumers?

Visualisations for Bayesian Additive Regression Trees

Compound-specific stable isotopes of amino acids reveal influences of trophic level and primary production sources on mercury concentrations in fishes from the Aleutian Islands, Alaska

Little and often: Causal inference machine learning demonstrates the benefits of homework for improving achievement in mathematics and science

SERT: A transformer based model for multivariate temporal sensor data with missing values for environmental monitoring

Vector time series modelling of turbidity in Dublin Bay

GP-BART: A novel Bayesian additive regression trees approach using Gaussian processes

Data-driven linear quadratic tracking based temperature control of a big area additive manufacturing system

Review of Clustering Methods for Functional Data

A Bayesian time series model for reconstructing hydroclimate from multiple proxies

Modelling Excess Zeros in Count Data: A New Perspective on Modelling Approaches

Visualizing Variable Importance and Variable Interaction Effects in Machine Learning Models

Towards an instant structure-property prediction quality control tool for additive manufactured steel using a crystal plasticity trained deep learning surrogate

Estimating the conditional probability of developing human papilloma virus related oropharyngeal cancer by combining machine learning and inverse Bayesian modelling

Differential effects of early growth conditions on colour-producing nanostructures revealed through small angle X-ray scattering and electron microscopy

Collaborators (13)

Chris Harrod

Fiona Doohan

Andrew Jackson

Rafael de Andrade Moral

Niamh Cahill

Eugene Kashdan

Ian Donohue

Mimi Zhang

Mariusz Cichoń

Lars Gustafsson

Paul Lincoln

Peter Gallagher

Seán B. Leen

How do I reach out?

Meet Kite AI