6 citations · 6 across the 6 of their papers we have counts for
6 papers
Dynamic Gaussian Processes and the Vanilla-SPDE Exchange
Rui-Yang Zhang, Lachlan Astfalck, Edward Cripps +2
Gaussian process inference is often limited by cubic computational costs, a challenge that becomes more pronounced in spatio-temporal settings where posterior inference is required…
Hybrid physics-data driven spectral forecasts of semisubmersible response
Ian Milne, Lachlan Astfalck, Matthew Zed +2
A framework for probabilistic forecasting of vessel motion is developed and validated for a semisubmersible operating in long period swell. Bayesian statistical methods are applied…
BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields
Rui-Yang Zhang, Lachlan Astfalck, Edward Cripps +2
We introduce a formal active learning methodology for guiding the placement of Lagrangian observers to infer time-dependent vector fields -- a key task in oceanography, marine scie…
Bias correction of quadratic spectral estimators
Lachlan Astfalck, Adam Sykulski, Edward Cripps
The three cardinal, statistically consistent, families of non-parametric estimators to the power spectral density of a time series are lag-window, multitaper and Welch estimators.…
Debiasing Welch's Method for Spectral Density Estimation
Lachlan C. Astfalck, Adam M. Sykulski, Edward J. Cripps
Welch's method provides an estimator of the power spectral density that is statistically consistent. This is achieved by averaging over periodograms calculated from overlapping seg…
Posterior Projection for Inference in Constrained Spaces
Lachlan Astfalck, Deborshee Sen, Sayan Patra +2
Estimation of parameters that obey specific constraints is crucial in statistics and machine learning; for example, when parameters are required to satisfy boundedness, monotonicit…