Submitted papers

  1. R. Rastelli and N. Friel. Optimal Bayesian estimators for latent variable cluster models. arXiv
  2. A. Benson and N. Friel. An adaptive MCMC method for multiple changepoint analysis with applications to large datasets. arXiv
  3. F. Maire, N. Friel, A. Mira and A.E. Raftery. Adaptive Incremental Mixture Markov chain Monte Carlo. arXiv
  4. F. Maire, N. Friel and P. Alquier. Light and Widely Applicable MCMC: Approximate Bayesian Inference for Large Datasets. arXiv


  5. Publications


  6. L. Bouranis, N. Friel and F. Maire. (2017) Bayesian inference for misspecified exponential random graph models. Social Networks, vol. 50, 98–108. arXiv DOI
  7. 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
  8. N. Friel, J.P. McKeone, C.J. Oates and A.N. Pettitt. Discussion of "A Bayesian information criterion for singular models" arXiv
  9. 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
  10. 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
  11. R. Rastelli, N. Friel and A.E. Raftery. (2016) Properties of Latent Variable Network Models. Network Science, vol 4, 407-432. arXiv DOI
  12. 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
  13. 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
  14. 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
  15. 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)
  16. 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
  17. 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
  18. 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
  19. 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.
  20. A. Caimo and N.Friel. (2014) Actor-based models for longitudinal networks. Encyclopedia of Social Network Analysis and Mining, Springer. pp 9-18. DOI
  21. 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.
  22. 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.
  23. N.Friel. (2013) Estimating the evidence for Gibbs random fields. Journal of Computational and Graphical Statistics. Vol. 22 pp. 518-532. arXiv DOI link.
  24. A. Caimo and N.Friel. (2013) Bayesian model selection for exponential random graph models. Social Networks. vol 35, pp. 11 - 24. arXiv pdf
  25. 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
  26. N.Friel. (2012) Bayesian inference for Gibbs random fields using composite likelihoods. Proceedings of the Winter Simulation Conference 2012 pdf.
  27. N. Friel and J. Wyse. (2012) Estimating the statistical evidence -- a review. Statistica Neerlandica, vol 66, 288-308. arXiv.
  28. 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.
  29. J. Wyse and N. Friel. (2012) Block clustering with collapsed latent block models. Statistics and Computing, vol 22, 415-428. arXiv.
  30. G. Behrens, N. Friel and M. Hurn. (2012) Tuning tempered transitions. Statistics and Computing, vol 22, 65-78. arXiv.
  31. 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.
  32. N. Friel. and J. Wyse. (2011) Discussion of Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods by M. Girolami and B. Calderhead, JRSSB.
  33. 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.
  34. H. Wang, N. Friel, F. Gosselin and P. Schyns. (2011) Efficient bubbles for visual categorization tasks. Vision research. vol 51, 1318-1323.
  35. N. Friel and A.N. Pettitt. (2011) Classification via distance nearest neighbours. Statistics and Computing. vol 21, 431-437. arXiv
  36. A. Caimo, N. Friel. (2011) Bayesian inference for exponential random graph models. Social Networks. vol 33, 41-55. arXiv.
  37. 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
  38. 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
  39. 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
  40. 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
  41. 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.
  42. 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
  43. N. Friel, H. Rue. (2007) Recursive computing and simulation-free inference for general factorizable models. Biometrika. vol. 94, 661-672. abstract
  44. 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
  45. P. Dellaportas, N. Friel, and G.O. Roberts. (2006) Bayesian model selection for partially observed diffusion models. Biometrika, vol.93, 809-825. abstract
  46. N. Friel and A.N. Pettitt. (2006) Discussion of Nested sampling by J. Skilling Valencia 8, Oxford University Press.
  47. 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.
  48. 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.
  49. 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.
  50. 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.
  51. E. Wit and N. Friel. (2003) Hidden Markov modelling of genomic interactions. Bulletin of the International Statistical Institute, 54th Session, August 2003.
  52. N. Friel. (2003) Discussion of Efficient construction of reversible jump MCMC proposal distributions by Brooks, S.P., Giudici, P. and Roberts, G.O. JRSSB.
  53. 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.
  54. N. Friel, I.S. Molchanov. (1999) A new thresholding technique based on random sets. Pattern Recognition, vol. 32, No. 9, 1507-1517.
  55. 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.
  56. 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.