Below we showcase several projects in which SIH has used bayesian methods. See all projects.
Wheat yield prediction with uncertainty estimates
Predicting crop yield using a range of proximal and remote sensor measurements is area of active research. Such predictions are important for optimisation of crop management (e.g. nitrogen application) and robust associated uncertainty estimates help to improve this process and understand its limitations. We wrote code implementing a Bayesian regression model with spatially correlated residuals for application to wheat crop yield forecasting using a range of sensor data. We used this to generate predictive maps of wheat yield with robust uncertainty bounds.
Bayesian Updating for Childhood Obesity Grant Proposal
SIH supported a grant proposal by the Centre for Translational Data Science, by demonstrating the value of using Bayesian modelling when collecting and analysing longitudinal data on childhood obesity. We built cross-sectional Bayesian variable selection models to select important factors and models for predicting children’s BMI, mental health and sleep quality across multiple ages, for each child in the Longitudinal Study of Australian Children (LSAC) study. A vector-autoregressive model was then applied to visualise the unexplained variation in the preceding models. We constructed visualisations to demonstrate the importance of understanding uncertainty over the course of data collection, and the potential for using Bayesian adaptive trials during collection.
1000-fold speedup in Dynamic Bayesian network model
A Bayesian network is a series of linear models fit to describe the relationships between different variables in a time series. If there are change points in how these variables are related, then the network is dynamic.
SIH helped the researcher by speeding up the R-package used to fit the dynamic bayesian network model by 1000x. The R-package is now available at https://github.com/FrankD/EDISON/tree/MultipleTimeSeries
Optimal Image Reconstruction for the SAMI Galaxy Survey
The SAMI Galaxy Survey is a large-scale observational program to target several thousand galaxies with the University of Sydney built Sydney-AAO Multi-object Integral field spectrograph (SAMI). A key data challenge is to optimally reconstruct a data cube from ~500 spectra taken at different spatial locations across a galaxy. The previous method resulted in undesirable artefacts due to under-sampling and the astronomical sources changing spatial location within the data due to differential atmospheric refraction. We have developed a novel method using probabilistic image fusion that delivers optimal combination of the spectral fibre bundle data into a cube with uniform image quality while maintaining spectral details. This innovative technology has further demonstrated capabilities to achieve super-resolution and is implemented as flexible software framework that can eventually be used by a wide range of worldwide telescopes.
Discharge against medical advice in the Sydney Children's Hospital Network
Patients who discharge against medical advice (DAMA) from hospital carry a significant risk of readmission and have increased rates of morbidity and mortality. Using five years of admissions and diagnosis data, we sought to identify the demographic, clinical and administrative characteristics of DAMA patients in the Sydney Children’s Hospital Network. Using a bayesian logistic regression framework, we found statistically significant predictors of DAMA in a given admission were hospital site, a mental health/behavioural diagnosis, Aboriginality, emergency rather than elective admissions, a gastrointestinal diagnosis and a history of previous DAMA. Identification of these predictors of DAMA provides opportunities for intervention at a practice and policy level in order to prevent adverse outcomes for patients.