University of Reading

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Determining the Veracity of Rumours on Twitter

While social networks can provide an ideal platform for up-to-date infor...
11/19/2016 ∙ by Georgios Giasemidis, et al. ∙ 0 ∙ share

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In the mood: the dynamics of collective sentiments on Twitter

We study the relationship between the sentiment levels of Twitter users ...
04/11/2016 ∙ by Nathaniel Charlton, et al. ∙ 0 ∙ share

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l1-norm Penalized Orthogonal Forward Regression

A l1-norm penalized orthogonal forward regression (l1-POFR) algorithm is...
09/04/2015 ∙ by Xia Hong, et al. ∙ 0 ∙ share

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Bayesian model comparison with un-normalised likelihoods

Models for which the likelihood function can be evaluated only up to a p...
04/01/2015 ∙ by Richard G. Everitt, et al. ∙ 0 ∙ share

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Lazier ABC

ABC algorithms involve a large number of simulations from the model of i...
01/21/2015 ∙ by Dennis Prangle, et al. ∙ 0 ∙ share

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Heterogeneous Tensor Decomposition for Clustering via Manifold Optimization

Tensors or multiarray data are generalizations of matrices. Tensor clust...
04/07/2015 ∙ by Yanfeng Sun, et al. ∙ 0 ∙ share

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Particle filters for applications in geosciences

Particle filters contain the promise of fully nonlinear data assimilatio...
07/27/2018 ∙ by Peter Jan van Leeuwen, et al. ∙ 0 ∙ share

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LFRic: Meeting the challenges of scalability and performance portability in Weather and Climate models

This paper describes LFRic: the new weather and climate modelling system...
09/19/2018 ∙ by S. V. Adams, et al. ∙ 0 ∙ share

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Perturbation Bounds for Monte Carlo within Metropolis via Restricted Approximations

The Monte Carlo within Metropolis (MCwM) algorithm, interpreted as a per...
09/25/2018 ∙ by Felipe Medina-Aguayo, et al. ∙ 0 ∙ share

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Precision of the ENDGame: Mixed-precision arithmetic in the iterative solver of the Unified Model

The Met Office's weather and climate simulation code the Unified Model i...
11/09/2018 ∙ by Christopher M Maynard, et al. ∙ 0 ∙ share

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Modelling the Demand and Uncertainty of Low Voltage Networks and the Effect of non-Domestic Consumers

The increasing use and spread of low carbon technologies are expected to...
01/24/2019 ∙ by Georgios Giasemidis, et al. ∙ 0 ∙ share

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Sparse Least Squares Low Rank Kernel Machines

A general framework of least squares support vector machine with low ran...
01/29/2019 ∙ by Manjing Fang, et al. ∙ 0 ∙ share

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Ensemble MCMC: Accelerating Pseudo-Marginal MCMC for State Space Models using the Ensemble Kalman Filter

Particle Markov chain Monte Carlo (pMCMC) is now a popular method for pe...
06/05/2019 ∙ by Christopher Drovandi, et al. ∙ 0 ∙ share

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Analysis of parallel I/O use on the UK national supercomputing service, ARCHER using Cray LASSi and EPCC SAFE

In this paper, we describe how we have used a combination of the LASSi t...
06/10/2019 ∙ by Andrew Turner, et al. ∙ 0 ∙ share

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Residual estimates for post-processors in elliptic problems

In this work we examine a posteriori error control for post-processed ap...
06/11/2019 ∙ by Andreas Dedner, et al. ∙ 0 ∙ share

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Spectral estimates for saddle point matrices arising in weak constraint four-dimensional variational data assimilation

We consider the large-sparse symmetric linear systems of equations that ...
08/21/2019 ∙ by Ieva Daužickaitė, et al. ∙ 0 ∙ share

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Coupling Matrix Manifolds and Their Applications in Optimal Transport

Optimal transport (OT) is a powerful tool for measuring the distance bet...
11/15/2019 ∙ by Dai Shi, et al. ∙ 0 ∙ share

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Lessons from reinforcement learning for biological representations of space

Neuroscientists postulate 3D representations in the brain in a variety o...
12/13/2019 ∙ by Alex Muryy, et al. ∙ 0 ∙ share

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Bayesian inference of dynamics from partial and noisy observations using data assimilation and machine learning

The reconstruction from observations of high-dimensional chaotic dynamic...
01/17/2020 ∙ by Marc Bocquet, et al. ∙ 0 ∙ share

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MCMC for Bayesian uncertainty quantification from time-series data

Many problems in science and engineering require uncertainty quantificat...
05/28/2020 ∙ by Philip Maybank, et al. ∙ 0 ∙ share

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Reconstructing regime-dependent causal relationships from observational time series

Inferring causal relations from observational time series data is a key ...
07/01/2020 ∙ by Elena Saggioro, et al. ∙ 0 ∙ share

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University of Reading

Featured Researchers

Dennis Prangle

Richard G. Everitt

Jakob Runge

Peter Jan van Leeuwen

Alberto Carrassi

Georgios Giasemidis

Xia Hong

Colin Singleton

Danica Vukadinovic Greetham

C. M. Maynard

Felipe Medina-Aguayo

Determining the Veracity of Rumours on Twitter

In the mood: the dynamics of collective sentiments on Twitter

l1-norm Penalized Orthogonal Forward Regression

Bayesian model comparison with un-normalised likelihoods

Lazier ABC

Heterogeneous Tensor Decomposition for Clustering via Manifold Optimization

Particle filters for applications in geosciences

LFRic: Meeting the challenges of scalability and performance portability in Weather and Climate models

Perturbation Bounds for Monte Carlo within Metropolis via Restricted Approximations

Precision of the ENDGame: Mixed-precision arithmetic in the iterative solver of the Unified Model

Modelling the Demand and Uncertainty of Low Voltage Networks and the Effect of non-Domestic Consumers

Sparse Least Squares Low Rank Kernel Machines

Ensemble MCMC: Accelerating Pseudo-Marginal MCMC for State Space Models using the Ensemble Kalman Filter

Analysis of parallel I/O use on the UK national supercomputing service, ARCHER using Cray LASSi and EPCC SAFE

Residual estimates for post-processors in elliptic problems

Spectral estimates for saddle point matrices arising in weak constraint four-dimensional variational data assimilation

Coupling Matrix Manifolds and Their Applications in Optimal Transport

Lessons from reinforcement learning for biological representations of space

Bayesian inference of dynamics from partial and noisy observations using data assimilation and machine learning

MCMC for Bayesian uncertainty quantification from time-series data

Reconstructing regime-dependent causal relationships from observational time series