most citedA synthetic likelihood-based Laplace approximation for efficient design of biological processes

7 citations · 9 across the 6 of their papers we have counts for

collaborators

6 papers

stat.ME2019

SSNdesign -- an R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks

Alan R. Pearse, James M. McGree, Nicholas A. Som +4

Streams and rivers are biodiverse and provide valuable ecosystem services. Maintaining these ecosystems is an important task, so organisations often monitor the status and trends i…

stat.AP2019

Factors associated with injurious from falls in people with early stage Parkinson's disease

Sarini Abdullah, James McGree, Nicole White +2

Falls are common in people with Parkinson's disease (PD) and have detrimental effects which can lower the quality of life. While studies have been conducted to learn about falling…

stat.AP20191 cited

Profile regression for subgrouping patients with early stage Parkinson's disease

Sarini Abdullah, James McGree, Nicole White +2

Falls are detrimental to people with Parkinson's Disease (PD) because of the potentially severe consequences to the patients' quality of life. While many studies have attempted to…

stat.AP20191 cited

Assessing the predictive ability of the UPDRS for falls classification in early stage Parkinson's disease

Sarini Abdullah, Nicole White, James McGree +2

Identification of risk factors associated with falls in people with Parkinson's Disease (PD) is important due to their high risk of falling. In this study, various ways of utilizin…

stat.ME20197 cited

A synthetic likelihood-based Laplace approximation for efficient design of biological processes

Mahasen Dehideniya, Antony M. Overstall, Chris C. Drovandi +1

Complex models used to describe biological processes in epidemiology and ecology often have computationally intractable or expensive likelihoods. This poses significant challenges…

stat.ME2016

An approach for finding fully Bayesian optimal designs using normal-based approximations to loss functions

Antony M. Overstall, James M. McGree, Christopher C. Drovandi

The generation of decision-theoretic Bayesian optimal designs is complicated by the significant computational challenge of minimising an analytically intractable expected loss func…