Submitted papers

  1. C. Lu, D. Durante, N. Friel. Zero-inflated stochastic block modeling of efficiency-security tradeoffs in weighted criminal networks. arXiv

  2. C. Lu, N. Friel. Bayesian Strategies for Repulsive Spatial Point Processes. arXiv

  3. L. Piancastelli, N. Friel. The clustered Mallow model. arXiv



    4. Publications


  4. L. Piancastelli, N. Friel, J. Vercelloni, K. Mengersen and A. Mira. (2024) A Bayesian latent allocation model for clustering compositional data with application to the Great Barrier Reef. Scientific Reports 14, 22123. DOI arXiv

  5. R. Rastelli, F. Maire and N. Friel. (2024) Computationally efficient inference for latent position network models. Electronic Journal of Statistics. Vol. 18, No. 1, 2531-2570. arXiv DOI

  6. J. Mulder, N. Friel and P. Leifeld. (2024) Bayesian Testing of Scientific Expectations Under Exponential Random Graph Models. Social Networks. Vol 78, 40-53. arXiv DOI

  7. H. Kaur, R. Rastelli, N. Friel, A.E. Raftery. (2023) Latent position network models. Chapter 36 in Sage Handbook of Social Network Analysis (2nd Edition), edited by J. McLevey, P. Carrington and J. Scott, Sage Publications. arXiv

  8. F. Basini, V. Tsouli, I. Ntzoufras and N. Friel. (2023). Assessing competitive balance in the English Premier League for over forty seasons using a stochastic block model. Journal of the Royal Statistical Society, Series A. Vol. 186. 530-556. arXiv DOI

  9. L.S.C. Piancastelli, N. Friel, W. Barreto-Souza and H. Ombao. (2023) Multivariate Conway-Maxwell-Poisson Distribution: Sarmanov Method and Doubly-Intractable Bayesian Inference. Journal of Computational and Graphical Statistics. Vol. 32. 483-500. arXiv DOI

  10. J. Meagher and N. Friel. (2022) Assessing epidemic curves for evidence of superspreading. Journal of the Royal Statistical Society, Series A. Vol. 185. 2179-2202. DOI arXiv

  11. A. Caimo, L. Bouranis, R. Krause, and N. Friel. Statistical Network Analysis with Bergm. Journal of Statistical Software (to appear). arXiv

  12. J.P. Gleeson, T.B. Murphy, J.D. O'Brien, N. Friel, N. Bargary, D.J.P. O'Sullivan. (2022) Calibrating COVID-19 SEIR models with time-varying effective contact rates. Philosophical Transactions of the Royal Society A. Vol. 380 (2214), 20210120. DOI arXiv

  13. A. Benson and N. Friel. (2021) Bayesian inference, model selection and likelihood estimation using fast rejection sampling: the Conway-Maxwell-Poisson distribution. Bayesian Analysis. Vol 16, pages 905–931. DOI arXiv

  14. L. Tan and N. Friel. (2020) Bayesian variational inference for exponential random graph models. Journal of Computational and Graphical Statistics. Vol 29, pages 910-928. DOI arXiv

  15. F. Maire, N. Friel, A. Mira and A.E. Raftery. (2019). Adaptive Incremental Mixture Markov chain Monte Carlo. Journal of Computational and Graphical Statistics. Vol 28, pages 790-805. DOI arXiv

  16. F. Maire, N. Friel and P. Alquier. (2019). Informed sub-sampling MCMC: Approximate Bayesian Inference for Large Datasets. Statistics and Computing. Vol. 29, Pages 449-482. DOI arXiv

  17. J. Stoehr, A. Benson and N. Friel. (2019). Noisy Hamiltonian Monte Carlo for doubly-intractable distributions. Journal of Computational and Graphical Statistics. Vol. 28, Pages 220-232. DOI arXiv

  18. A. Boland, N. Friel and F. Maire. (2018) Efficient MCMC for Gibbs Random Fields using pre-computation. Electronic Journal of Statistics. Volume 12, Number 2, 4138-4179. arXiv DOI

  19. L. Bouranis, N. Friel and F. Maire. (2018) Model comparison for Gibbs random fields using noisy reversible jump Markov chain Monte Carlo. Computational Statistics and Data Analysis. Volume 128, Pages 221-241. arXiv DOI

  20. R. Rastelli, P. Latouche, and N. Friel. (2018). Choosing the number of groups in a latent stochastic block model for dynamic networks. Network Science. Vol. 6, Pages 469-493. arXiv DOI

  21. A. Benson and N. Friel. (2018) An adaptive MCMC method for multiple changepoint analysis with applications to large datasets. Electronic Journal of Statistics. Volume 12, 3365-3396.arXiv DOI

  22. L. Bouranis, N. Friel and F. Maire. (2018). Bayesian model selection for exponential random graph models via adjusted pseudolikelihoods. Journal of Computational and Graphical Statistics. Vol. 27, Pages 516-528. DOI arXiv

  23. R. Rastelli and N. Friel. (2018) Optimal Bayesian estimators for latent variable cluster models. Statistics and Computing. Volume 28, Issue 6, pp 1169–1186. arXiv DOI R code

  24. L. Spezia, N.Friel and A. Gimona. (2018). Spatial hidden Markov models and species distributions. Journal of Applied Statistics. Vol. 45, Pages 1595-1615. DOI

  25. L. Bouranis, N. Friel and F. Maire. (2017) Bayesian inference for misspecified exponential random graph models. Social Networks, vol. 50, 98–108. arXiv DOI

  26. T. Ryan, J. Wyse and N. Friel. (2017) Bayesian model selection for the latent position cluster model for Social Networks. Network Science, vol. 5, 70-91. arXiv DOI

  27. N. Friel, J.P. McKeone, C.J. Oates and A.N. Pettitt. Discussion of "A Bayesian information criterion for singular models" arXiv

  28. N. Friel, J.P. McKeone, C.J. Oates and A.N. Pettitt. (2017) Investigation of the widely applicable Bayesian information criterion. Statistics and Computing, vol. 27, 833-844. arXiv DOI

  29. J. Wyse, N.Friel and P. Latouche. (2017) Inferring structure in bipartite networks using the latent block model and exact ICL . Network Science, vol. 5, 45-69. arXiv DOI

  30. R. Rastelli, N. Friel and A.E. Raftery. (2016) Properties of Latent Variable Network Models. Network Science, vol 4, 407-432. arXiv DOI

  31. N. Friel, R. Rastelli, J. Wyse and A.E. Raftery. (2016) Interlocking directorates in Irish companies using a latent space model for bipartite networks. Proceedings of the National Academy of Sciences. vol. 113, no. 24, 6629-6634. DOI

  32. S. Thiemichen, N. Friel, A. Caimo and G. Kauermann. (2016) Bayesian Exponential Random Graph Models with Nodal Random Effects. Social Networks, vol. 46, 11 - 28. arXiv DOI

  33. N. Friel, A. Mira and C.J. Oates. (2016) Exploiting Multi-Core Architectures for Reduced-Variance Estimation with Intractable Likelihoods. Bayesian Analysis vol. 11, 215 - 245. arXiv DOI

  34. P. Alquier, N.Friel, R. Everitt and A. Boland. (2016) Noisy Monte Carlo: Convergence of Markov chains with approximate transition kernels. Statistics and Computing, vol. 26, 29 - 47. arXiv, DOI. (See a related blog post by Richard Everitt)

  35. M. Bertoletti, N. Friel and R. Rastelli. (2015) Choosing the number of components in a finite mixture model using an exact Integrated Completed Likelihood criteria. Metron, vol. 73, 177 - 199. (Special issue on "Latent Variable Models for the Analysis of Socio-Economic Data") arXiv

  36. J. Stoehr and N.Friel. (2015) Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields. AISTATS, Journal of Machine Learning Research: W&CP, pp. 921-929. pdf

  37. J.W. Yoon and N.Friel. (2015) Efficient estimation of the number of neighbours in probabilistic nearest neighbour classification. Neurocomputing. vol. 149, 1098-1108. arXiv DOI

  38. A. Caimo and N.Friel. (2014) Bergm: Bayesian inference for exponential random graphs using R. Journal of Statistical Software. vol. 61, issue 2. pdf arXiv 23pp.

  39. A. Caimo and N.Friel. (2014) Actor-based models for longitudinal networks. Encyclopedia of Social Network Analysis and Mining, Springer. pp 9-18. DOI

  40. S. Pandolfi, F. Bartolucci and N.Friel. (2014) A generalized multiple-try version of the reversible jump algorithm. Computational Statistics and Data Analysis. vol. 72, 298 - 314. arXiv DOI link.

  41. N.Friel, M.A. Hurn and J. Wyse. (2014) Improving power posterior estimation of statistical evidence. Statistics and Computing. vol. 24, 709–723. arXiv DOI link.

  42. N.Friel. (2013) Estimating the evidence for Gibbs random fields. Journal of Computational and Graphical Statistics. Vol. 22 pp. 518-532. arXiv DOI link.

  43. A. Caimo and N.Friel. (2013) Bayesian model selection for exponential random graph models. Social Networks. vol 35, pp. 11 - 24. arXiv pdf

  44. A. McDaid, B. Murphy, N.Friel and N. Hurley. (2013) Improved Bayesian inference for the stochastic block model with application to large networks. Computational Statistics and Data Analysis. vol 60, pp. 12 - 31. arXiv

  45. N.Friel. (2012) Bayesian inference for Gibbs random fields using composite likelihoods. Proceedings of the Winter Simulation Conference 2012 pdf.

  46. N. Friel and J. Wyse. (2012) Estimating the statistical evidence -- a review. Statistica Neerlandica, vol 66, 288-308. arXiv.

  47. A. McDaid, T.B. Murphy, N. Friel and N. Hurley. (2012) Model-based clustering in networks with Stochastic Community Finding. Proceedings of COMPSTAT 2012. Pages 549-560.

  48. J. Wyse and N. Friel. (2012) Block clustering with collapsed latent block models. Statistics and Computing, vol 22, 415-428. arXiv.

  49. G. Behrens, N. Friel and M. Hurn. (2012) Tuning tempered transitions. Statistics and Computing, vol 22, 65-78. arXiv.

  50. J. Wyse, N. Friel and H. Rue. (2011) Approximate simulation free multiple changepoint analysis with Gaussian Markov random field segment models. (with discussion) Bayesian Analysis, vol 6, 501-528. arXiv.

  51. N. Friel. and J. Wyse. (2011) Discussion of Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods by M. Girolami and B. Calderhead, JRSSB.

  52. M. Hall and N. Friel. (2011) Mortality projections using generalised additive models with applications to annuity values for the Irish population. Annals of Actuarial Science. vol 5, 19-32.

  53. H. Wang, N. Friel, F. Gosselin and P. Schyns. (2011) Efficient bubbles for visual categorization tasks. Vision research. vol 51, 1318-1323.

  54. N. Friel and A.N. Pettitt. (2011) Classification via distance nearest neighbours. Statistics and Computing. vol 21, 431-437. arXiv

  55. A. Caimo, N. Friel. (2011) Bayesian inference for exponential random graph models. Social Networks. vol 33, 41-55. arXiv.

  56. P. Tong et al. (2010) Sequencing and analysis of an Irish human genome. Genome Biology, 11: R91 DOI: 10.1186/gb-2010-11-9-r91

  57. H. Austad and N.Friel. (2010) Deterministic Bayesian inference for the p* model. AISTATS, Journal of Machine Learning Research: W&CP 9, 41-48. pdf

  58. S. Pandolfi and F. Bartolucci and N.Friel. (2010) A generalization of the Multiple-try Metropolis algorithm for Bayesian estimation and model selection. AISTATS, Journal of Machine Learning Research: W&CP 9, 251-258. pdf

  59. N. Friel, A.N. Pettitt, R. Reeves, and E. Wit. (2009) Bayesian inference in hidden Markov random fields for binary data defined on large lattices. Journal of Computational and Graphical Statistics. vol 18, 243-261. abstract

  60. M. Salter-Townshend, J. Haslett, N. Friel. (2009) Discussion of Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations by Rue, H., Chopin, N. and Martino, S. JRSSB.

  61. N. Friel, A.N. Pettitt. (2008) Marginal likelihood estimation via power posteriors. Journal of the Royal Statistical Society, Series B. vol 70, 589-607. abstract

  62. N. Friel, H. Rue. (2007) Recursive computing and simulation-free inference for general factorizable models. Biometrika. vol. 94, 661-672. abstract

  63. P.G. Ridall, A.N. Pettitt, N. Friel, P.A. McCombe and R. Henderson. (2007) Motor unit number estimation using reversible jump Markov chain Monte Carlo (with discussion). Journal of the Royal Statistical Society, Series C., vol 56, 235-269.
    (Read before the Royal Statistical Society, 29th of November 2006.) abstract

  64. P. Dellaportas, N. Friel, and G.O. Roberts. (2006) Bayesian model selection for partially observed diffusion models. Biometrika, vol.93, 809-825. abstract

  65. N. Friel and A.N. Pettitt. (2006) Discussion of Nested sampling by J. Skilling Valencia 8, Oxford University Press.

  66. P.J. Ridall, A.N. Pettitt, N. Friel, P.A. McCombe and R. Henderson. (2005) A Bayesian method for motor unit number estimation for the tracking of amyotropic lateral sclerosis using reversible jump Markov chain Monte Carlo. 20th International Workshop on Statistical Modelling, Sydney, 397-410.

  67. N. Friel, and A.N. Pettitt. (2004) Likelihood estimation and inference for the autologistic model. Journal of computational and Graphical Statistics, vol. 13, 232-246.

  68. S.P. Brooks, N. Friel, and R. King. (2003). Classical model selection via simulated annealing. Journal of the Royal Statistical Society, Series B, vol. 65, part 2, 503-521.

  69. A.N. Pettitt, N. Friel, and R. Reeves. (2003) Efficient calculation of the normalisation constant of the autologistic model on the lattice. Journal of the Royal Statistical Society, Series B, vol. 65, part 1, 235-247.

  70. E. Wit and N. Friel. (2003) Hidden Markov modelling of genomic interactions. Bulletin of the International Statistical Institute, 54th Session, August 2003.

  71. N. Friel. (2003) Discussion of Efficient construction of reversible jump MCMC proposal distributions by Brooks, S.P., Giudici, P. and Roberts, G.O. JRSSB.

  72. S.J. Grant, et al. (2003) The effect of ball carrying method on sprint speed in Rugby union football players. Journal of Sports Science, vol. 21, 12, 1009-1015.

  73. N. Friel, I.S. Molchanov. (1999) A new thresholding technique based on random sets. Pattern Recognition, vol. 32, No. 9, 1507-1517.

  74. N. Friel, I.S. Molchanov. (1998)Distances between grey-scale images. In H. Heijmans and J. Roerdink, editors, Mathematical Morphology and its Applications to Image and Signal processing, 283-291, Dordrecht.

  75. N. Friel, I.S. Molchanov. (1998) A class of error metrics for grey-scale images. In Proc. SPIE, Mathematical Modeling and Estimation Techniques in Computer Vision. Vol. 3457, 194-202, San Diego, July, 1998.