Search Results for author: Oliver M. Cliff

Found 8 papers, 2 papers with code

Unifying Pairwise Interactions in Complex Dynamics

1 code implementation28 Jan 2022 Oliver M. Cliff, Joseph T. Lizier, Naotsugu Tsuchiya, Ben D. Fulcher

Scientists have developed hundreds of techniques to measure the interactions between pairs of processes in complex systems.

Causal Inference Time Series Analysis

Simulating transmission scenarios of the Delta variant of SARS-CoV-2 in Australia

no code implementations14 Jul 2021 Sheryl L. Chang, Oliver M. Cliff, Cameron Zachreson, Mikhail Prokopenko

An outbreak of the Delta (B. 1. 617. 2) variant of SARS-CoV-2 that began around mid-June 2021 in Sydney, Australia, quickly developed into a nation-wide epidemic.

How will mass-vaccination change COVID-19 lockdown requirements in Australia?

no code implementations12 Mar 2021 Cameron Zachreson, Sheryl L. Chang, Oliver M. Cliff, Mikhail Prokopenko

For realistic scenarios in which herd immunity is not achieved, we simulate the effects of mass-vaccination on epidemic growth rate, and investigate the requirements of lockdown measures applied to curb subsequent outbreaks.

Modelling transmission and control of the COVID-19 pandemic in Australia

no code implementations23 Mar 2020 Sheryl L. Chang, Nathan Harding, Cameron Zachreson, Oliver M. Cliff, Mikhail Prokopenko

We then apply the model to compare several intervention strategies, including restrictions on international air travel, case isolation, social distancing with varying levels of compliance, and school closures.

Assessing the Significance of Directed and Multivariate Measures of Linear Dependence Between Time Series

1 code implementation9 Mar 2020 Oliver M. Cliff, Leonardo Novelli, Ben D. Fulcher, James M. Shine, Joseph T. Lizier

Inferring linear dependence between time series is central to our understanding of natural and artificial systems.

Methodology Information Theory Information Theory Statistics Theory Data Analysis, Statistics and Probability Neurons and Cognition Applications Statistics Theory

Inferring Coupling of Distributed Dynamical Systems via Transfer Entropy

no code implementations2 Nov 2016 Oliver M. Cliff, Mikhail Prokopenko, Robert Fitch

In this work, we are interested in structure learning for a set of spatially distributed dynamical systems, where individual subsystems are coupled via latent variables and observed through a filter.

An Information Criterion for Inferring Coupling in Distributed Dynamical Systems

no code implementations23 May 2016 Oliver M. Cliff, Mikhail Prokopenko, Robert Fitch

The behaviour of many real-world phenomena can be modelled by nonlinear dynamical systems whereby a latent system state is observed through a filter.

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