activity
19992019
most citedBetter Nonlinear Models from Noisy Data: Attractors with Maximum Likelihood

99 citations · 160 across the 7 of their papers we have counts for

collaborators

7 papers

physics.data-an2019

Shadowing the rotating annulus. Part II: Gradient descent in the perfect model scenario

Roland M. B. Young, Roman Binter, Falk Niehörster +2

Shadowing trajectories are model trajectories consistent with a sequence of observations of a system, given a distribution of observational noise. The existence of such trajectorie…

stat.ME2016★ 2 cited

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…

physics.data-an2016★ 19 cited

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…

physics.data-an2014★ 1 cited

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…

physics.data-an2012★ 18 cited

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…

nlin.CD2004★ 21 cited

Consistent nonlinear dynamics: identifying model inadequacy

Patrick E. McSharry, Leonard A. Smith

Empirical modelling often aims for the simplest model consistent with the data. A new technique is presented which quantifies the consistency of the model dynamics as a function of…