2 citations · 3 across the 8 of their papers we have counts for
9 papers
GeoMix: A Bayesian Framework for Stratigraphic and Geotechnical Inference
Bradley Wakefield, Michael Bertolacci, David Gunawan +3
Accurate characterisation of subsea sediments is essential for engineering projects such as offshore wind farms, but the challenging environment and large spatial domains involved…
Stacked Ensemble Learning for Abdominal Aortic Aneurysm Segmentation in CT Angiography
Joshua Fry, Sajjad Arzemanzadeh, Saeideh Sekhavat +9
Abdominal aortic aneurysm (AAA) rupture risk assessment increasingly relies on patient-specific biomechanical computations, which require accurate three-dimensional aneurysm geomet…
WOMBAT v2.S: A Bayesian inversion framework for attributing global CO flux components from multiprocess data
Josh Jacobson, Michael Bertolacci, Andrew Zammit-Mangion +2
Contributions from photosynthesis and other natural components of the carbon cycle present the largest uncertainties in our understanding of carbon dioxide (CO) sources and sin…
GeoWarp: Warped spatial processes for inferring subsea sediment properties
Michael Bertolacci, Andrew Zammit-Mangion, Juan Valderrama Giraldo +3
For offshore structures like wind turbines, subsea infrastructure, pipelines, and cables, it is crucial to quantify the properties of the seabed sediments at a proposed site. Howev…
Inferring changes to the global carbon cycle with WOMBAT v2.0, a hierarchical flux-inversion framework
Michael Bertolacci, Andrew Zammit-Mangion, Andrew Schuh +5
The natural cycles of the surface-to-atmosphere fluxes of carbon dioxide (CO) and other important greenhouse gases are changing in response to human influences. These changes n…
From Many to One: Consensus Inference in a MIP
Noel Cressie, Michael Bertolacci, Andrew Zammit-Mangion
A Model Intercomparison Project (MIP) consists of teams who each estimate the same underlying quantity (e.g., temperature projections to the year 2070), and the spread of the estim…
WOMBAT: A fully Bayesian global flux-inversion framework
Andrew Zammit-Mangion, Michael Bertolacci, Jenny Fisher +4
WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-gases) is a fully Bayesian hierarchical statistical framework for flux inversion of trace gases from flask, in…
AdaptSPEC-X: Covariate Dependent Spectral Modeling of Multiple Nonstationary Time Series
Michael Bertolacci, Ori Rosen, Edward Cripps +1
We present a method for the joint analysis of a panel of possibly nonstationary time series. The approach is Bayesian and uses a covariate-dependent infinite mixture model to incor…
A comparison of methods for modeling marginal non-zero daily rainfall across the Australian continent
Michael Bertolacci, Edward Cripps, Ori Rosen +1
Naveau et al. (2016) have recently developed a class of methods, based on extreme-value theory (EVT), for capturing low, moderate, and heavy rainfall simultaneously, without the ne…