19 citations · 41 across the 6 of their papers we have counts for
6 papers
A Distribution-to-Distribution Neural Probabilistic Forecasting Framework for Dynamical Systems
Tianlin Yang, Hailiang Du, Louis Aslett
Probabilistic forecasts provide a principled representation of forecast uncertainty through predictive distributions. Directly propagating predictive distributions with conventiona…
Beyond Strictly Proper Scoring Rules: The Importance of Being Local
Hailiang Du
The evaluation of probabilistic forecasts plays a central role both in the interpretation and in the use of forecast systems and their development. Probabilistic scores (scoring ru…
On the Design and use of Ensembles of Multi-model Simulations for Forecasting
Sarah Higgins, Hailiang Du, Leonard A. Smith
Probability forecasting is common in the geosciences, the finance sector, and elsewhere. It is sometimes the case that one has multiple probability-forecasts for the same target. H…
Multi-model Cross Pollination in Time
Hailiang Du, Leonard A. Smith
Predictive skill of complex models is often not uniform in model-state space; in weather forecasting models, for example, the skill of the model can be greater in populated regions…
Rising Above Chaotic Likelihoods
Hailiang Du, Leonard A. Smith
Berliner (Likelihood and Bayesian prediction for chaotic systems, J. Am. Stat. Assoc. 1991) identified a number of difficulties in using the likelihood function within the Bayesian…
Parameter Estimation Through Ignorance
Hailiang Du, Leonard A. Smith
Dynamical modelling lies at the heart of our understanding of physical systems. Its role in science is deeper than mere operational forecasting, in that it allows us to evaluate th…