Imperial College London

Professor Nick Heard

Faculty of Natural SciencesDepartment of Mathematics

Chair in Statistics



+44 (0)20 7594 1490n.heard Website




543Huxley BuildingSouth Kensington Campus





Nick Heard is Chair in Statistics in the Department of Mathematics at Imperial College. His research interests center around computational Bayesian infererence, clustering and changepoint analysis, with a focus on application to large dynamic networks, such as computer networks or social networks, and bioinformatics problems.

Prof Nick Heard's personal web page can be found at

Selected Publications

Journal Articles

Hallgren KL, Heard NA, Turcotte MJM, 2023, Changepoint detection on a graph of time series, Bayesian Analysis, Vol:-, ISSN:1936-0975, Pages:1-28

Sanna Passino F, Heard N, 2022, Latent structure blockmodels for Bayesian spectral graph clustering, Statistics and Computing, Vol:32, ISSN:0960-3174

Sanna Passino F, Heard NA, Rubin-Delanchy P, 2021, Spectral clustering on spherical coordinates under the degree-corrected stochastic blockmodel, Technometrics, Vol:64, ISSN:0040-1706, Pages:346-357

Heard NA, 2021, Standardized partial sums and products of p-values, Journal of Computational and Graphical Statistics, Vol:31, ISSN:1061-8600, Pages:563-573

Sanna Passino F, Turcotte MJM, Heard NA, 2021, Graph link prediction in computer networks using Poisson matrix factorisation, Annals of Applied Statistics, Vol:16, ISSN:1932-6157, Pages:1313-1332

Sanna Passino F, Heard N, 2020, Bayesian estimation of the latent dimension and communities in stochastic blockmodels, Statistics and Computing, Vol:30, ISSN:0960-3174, Pages:1291-1307

Price-Williams M, Heard N, 2020, Nonparametric self-exciting models for computer network traffic, Statistics and Computing, Vol:30, ISSN:0960-3174, Pages:209-220

Metelli S, Heard N, 2019, On Bayesian new edge prediction and anomaly detection in computer networks, Annals of Applied Statistics, Vol:13, ISSN:1932-6157, Pages:2586-2610

Bolton A, Heard N, 2018, Malware family discovery using reversible jump MCMC sampling of regimes, Journal of the American Statistical Association, Vol:113, ISSN:0162-1459, Pages:1490-1502

Heard N, Rubin-Delanchy P, 2018, Choosing between methods of combining p-values, Biometrika, Vol:105, ISSN:0006-3444, Pages:239-246

Heard NA, Turcotte MJM, 2017, Adaptive sequential Monte Carlo for multiple changepoint analysis, Journal of Computational and Graphical Statistics, Vol:26, ISSN:1061-8600, Pages:414-423


Heard N, 2021, An introduction to Bayesian inference, methods and computation, ISBN:9783030828073

More Publications