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Nicholas A. Heard
Researcher at Imperial College London
Publications - 66
Citations - 1310
Nicholas A. Heard is an academic researcher from Imperial College London. The author has contributed to research in topics: Anomaly detection & Cluster analysis. The author has an hindex of 14, co-authored 64 publications receiving 1120 citations. Previous affiliations of Nicholas A. Heard include Los Alamos National Laboratory & University of Bristol.
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A quantitative study of gene regulation involved in the immune response of Anopheline mosquitoes: An application of Bayesian hierarchical clustering of curves
TL;DR: A Bayesian model-based hierarchical clustering algorithm is introduced for curve data to investigate mechanisms of regulation in the genes concerned and reveals structure within the data not captured by other approaches.
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Bayesian anomaly detection methods for social networks
TL;DR: The first stage uses simple, conjugate Bayesian models for discrete time counting processes to track the pairwise links of all nodes in the graph to assess normality of behavior, and the second stage applies standard network inference tools on a greatly reduced subset of potentially anomalous nodes.
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Bayesian cluster identification in single-molecule localization microscopy data
Patrick Rubin-Delanchy,Garth L. Burn,Juliette Griffié,David Williamson,Nicholas A. Heard,Andrew P. Cope,Dylan M. Owen +6 more
TL;DR: A model-based Bayesian approach is presented to evaluate molecular cluster assignment proposals, generated in this study by analysis based on Ripley's K function, and takes full account of the individual localization precisions calculated for each emitter.
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Choosing between methods of combining p-values
TL;DR: A diverse range of p-value combination methods appear in the literature, each with different statistical properties as mentioned in this paper, and the final choice used in a meta-analysis can appear arbitrary, as if all effort has been expended building the models that gave rise to the pvalues.
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Generalized monotonic regression using random change points
TL;DR: A procedure for generalized monotonic curve fitting that is based on a Bayesian analysis of the isotonic regression model and uses Markov chain Monte Carlo simulation to draw samples from the unconstrained model space and retain only those samples for which themonotonic constraint holds.