Publications

Books

  1. Scrucca, L., Fraley, C., Murphy, T.B. and Raftery, A.E. (2023) Model-Based Clustering, Classification, and Density Estimation Using mclust in R, CRC Press. http://dx.doi.org/10.1201/9781003277965
  2. Bouveyron, C., Celeux, G., Murphy, T.B. and Raftery, A.E. (2019) Model-based Clustering and Classification for Data Science, Cambridge University Press. http://dx.doi.org/10.1017/9781108644181

Papers

    2026

  1. Rojas-Gómez, P., Pariyani, R., Dineen, M., Lynch, D., Bateman, L., Roche, E., Maguire, A.R., McCarthy, N.A., Murphy, T.B., O’Mahony, J.A., O’Callaghan, T.F. (2026) The MetaBó-Bainne Study – characterisation of the milk metabolome from a seasonal pasture-based dairy system using \(^1\)H-NMR spectroscopy. Food Chemistry, To appear. http://dx.doi.org/10.1016/j.foodchem.2026.149492

  2. Carlson, J.T., Murphy, T.B., O’Grady, L., Brock, J., Guelbenzu-Gonzalo, M.P., Graham, D.A., McGrath, G., Tratalos, J.A., de Andrade Moral, R., Mimnagh, N., Field, N.L., Mee, J.F., Barrett, D.J., Lane, E.A., McAloon, C.G. (2026) Using herd frailty estimates from survival models in a mortality-based syndromic surveillance system. Preventive Veterinary Medicine, To appear. http://dx.doi.org/10.1016/j.prevetmed.2026.106785

    3. 2025

  3. Berrettini, M., Galimberti, G., Ranciati, S. and Murphy, T.B. (2025) Modelling football players field position via mixture of Gaussians with flexible weights. Statistical Models and Learning Methods for Complex Data. Giordano, G., La Rocca, M., Niglio, M., Restaino, M. and Vichi, M. (Eds.), Springer, 17–24. http://dx.doi.org/10.1007/978-3-031-84702-8_3
  4. Seri, E., Rocci, R. and Murphy, T.B. (2025) Partial membership models for soft clustering of multivariate football player performance data. Computional Statistics, To appear. http://dx.doi.org/10.1007/s00180-025-01655-w
  5. Bartl, M., Murphy, T.B. and Leavy, S. (2025) Adapting psycholinguistic research for LLMs: Gender-inclusive language in a coreference context. Proceedings of the 6th Workshop on Gender Bias in Natural Language Processing (GeBNLP), Faleńska, A., Basta, C., Costa-jussà, M., Stańczak, K. and Nozza, D. (Eds.), 451–467. http://dx.doi.org/10.18653/v1/2025.gebnlp-1.38
  6. Jacques, J. and Murphy, T.B. (2025) Model-based clustering and variable selection for multivariate count data. Computo, July. http://dx.doi.org/10.57750/6v7b-8483
  7. Murphy, T.B. (2025) \(k\)-means clustering. International Encyclopedia of Statistical Science (2nd Ed.). Lovric, M. (Ed.), Springer, 1303–1305. http://dx.doi.org/10.1007/978-3-662-69359-9_309
  8. Liu, W., Murphy. T.B. and Brennan, L. (2025) Simplex-structured Matrix Factorisation: Application of Soft Clustering to Metabolomic Data. Scientific Reports, 15, 17817. http://dx.doi.org/10.1038/s41598-025-02361-9
  9. Berry, D.P. and Murphy, T.B. (2025) Relative effectiveness of genomic selection of candidate dams versus genomic selection of born progeny in achieving genetic gain. JDS Communications, 6(3), 345–349. http://dx.doi.org/10.3168/jdsc.2024-0705
  10. Rojas-Gómez, P., Pariyani, R., Bateman, L.M., Lynch, D., Timlin, M., Dineen, M., McCarthy, N.A., Brodkorb, A., Maguire, A.R., O’Donovan, M., Hennessy, D., Murphy, T.B., O’Mahony, J.A., O’Callaghan, T.F. (2025) Impact of proportion of pasture in the cow diet and seasonality on the milk metabolome as determined by \(^1\)H-NMR. Journal of Dairy Science, 108(5), 4659–4673. http://dx.doi.org/10.3168/jds.2024-26168
  11. Casa, A., Murphy, T.B. and Fop, M. (2025) Sparse partial membership models with applications in food science. Methodological and Applied Statistics and Demography I, Pollice, A. and Mariani, P. (Eds.), Springer. 99–104. http://dx.doi.org/10.1007/978-3-031-64346-0_17

  12. O’Donovan, C.M., Ravi Chandra Nori, S., Shanahan, F., Celentano, G., Murphy, T.B., Cotter, P., O’Suliivan, O. (2025) Temporal stability and lack of variance in microbiome composition and functionality in fit recreational athletes. Scientific Reports, 15, 5619. http://dx.doi.org/10.1038/s41598-025-88723-9

    13. 2024

  13. Berrettini, M., Galimberti, G., Ranciati, S. and Murphy, T.B. (2024) Identifying Brexit voting patterns in the British House of Commons: an analysis based on Bayesian mixture models with flexible concomitant covariate effects. Journal of the Royal Statistical Society, Series C, 73(3), 621–638. http://dx.doi.org/10.1093/jrsssc/qlae004
  14. Majumdar, K., Silva, R., Perry, A.S., Watson, R.W., Rau, A., Jaffrezic, F., Murphy, T.B. and Gormley, I.C. (2024) A novel family of beta mixture models for the differential analysis of DNA methylation data: an application to prostate cancer. PLoS ONE, 19(12), e0314014. http://dx.doi.org/10.1371/journal.pone.0314014

  15. Duggan, J., Andrade, J., Murphy, T.B., Gleeson, J.P., Walsh, C. and Nolan, P. (2024) An age-cohort simulation model for generating COVID-19 scenarios: A study from Ireland’s pandemic response. European Journal of Operational Research, 313(1), 343–358. http://dx.doi.org/10.1016/j.ejor.2023.08.011

    16. 2023

  16. Gormley, I.C., Murphy, T.B. and Raftery, A.E. (2023) Model-based clustering. Annual Review of Statistics and Its Application, 10, 573–595. http://dx.doi.org/10.1146/annurev-statistics-033121-115326
  17. Alkema, L., Raftery, A.E. and Murphy, T.B. (2023) Interview with Adrian E. Raftery. International Statistical Review, 91(3), 349–367. http://dx.doi.org/10.1111/INSR.12557
  18. Kokina, T., Peláez-Zapata, D.S., Murphy, T.B. and Dias, F. (2023) Improving the sea state forecasts by using local wave observations and the ensembleBMA software. Environmental Data Science, 2(e36), 1–27. http://dx.doi.org/10.1017/eds.2023.31
  19. Smith, R., Dias, F., Facciolo, G. and Murphy, T.B. (2023) Pre-computation of image features for the classification of dynamic properties in breaking waves. European Journal of Remote Sensing, 56(1), 2163707. http://dx.doi.org/10.1080/22797254.2022.2163707

  20. Monacelli, G., Zhang, L., Schlee, W., Langguth, B., Ward, T. and Murphy, T.B. (2023) Adaptive data collection for intra-individual studies affected by adherence. Biometrical Journal, 65(7), 2200203. http://dx.doi.org/10.1002/bimj.202200203

    21. 2022

  21. Casa, A., O’Callaghan, T.F. and Murphy, T.B. (2022) Parsimonious Bayesian factor analysis for modelling latent structures in spectroscopy data. Annals of Applied Statistics, 16(4), 2417–2436. http://dx.doi.org/10.1214/21-AOAS1597
  22. Ng, T.L.J. and Murphy, T.B. (2022) Model-based clustering for random hypergraphs. Advances in Data Analysis & Classification, 16(3), 691–723. http://dx.doi.org/10.1007/s11634-021-00454-7
  23. Fop, M., Alexandre, P-A., Murphy, T.B. and Bouveyron, C. (2022) Unobserved classes and extra variables in discriminant analysis. Advances in Data Analysis & Classification, 16(1), 55–92. http://dx.doi.org/10.1007/s11634-021-00474-3

  24. Gleeson, J.P., Murphy, T.B., O’Brien, J.D., Friel, N., Bargary, N., O’Sullivan, D.J.P. (2022) Calibrating COVID-19 SEIR models with time-varying effective contact rates. Philosophical Transactions of the Royal Society A, 380(2214), 20210120. http://dx.doi.org/10.1098/rsta.2021.0120

    25. 2021

  25. Ng, T.L.J., Murphy, T.B., McCormick, T., Fosdick, B. and Westling, T. (2021) Modeling the social media relationships of Irish politicians using a generalized latent space stochastic blockmodel. Annals of Applied Statistics, 15(4), 1923–1944. http://dx.doi.org/10.1214/21-AOAS1483
  26. McAloon, C., Wall, P., Butler, F., Codd, M., Gormley, E., Walsh, C., Murphy, T.B., Nolan, P., Smyth, B., O’Brien, K., Teljeur, C., Green, M., O’Grady, L., Culhane, K. and More, S. (2021) Numbers of close contacts of individuals infected with SARS-CoV-2 and their association with government intervention strategies. BMC Public Health, 21, 2238. http://dx.doi.org/10.1186/s12889-021-12318-y
  27. Frizzarin, M., O’Callaghan, T.F., Murphy, T.B., Hennessy, D. and Casa, A. (2021) Application of machine learning methods to milk mid infrared spectra for discrimination of cows milk from pasture or TMR diets. Journal of Dairy Science, 104(12), 12394–12402. http://dx.doi.org/10.3168/jds.2021-20812
  28. Ng, T.L.J. and Murphy, T.B. (2021) Weighted stochastic block model. Statistical Methods and Applications, 30(5), 1365–1398. http://dx.doi.org/10.1007/s10260-021-00590-6
  29. Murphy, K., Murphy, T.B., Piccaretta, R. and Gormley, I.C. (2021) Clustering longitudinal life-course sequences using mixtures of exponential-distance models. Journal of the Royal Statistical Society, Series A., 184(4), 1414–1451. http://dx.doi.org/10.1111/rssa.12712
  30. Moghaddam, S., Jalali, A., O’Neill, A., Murphy, L., Gorman, L., Reilly, A-M., Heffernan, Á., Lynch, T., Power, R., O’Malley, K.J., Tasken, K.A., Berge, V., Solhaug, V-A., Klocker, H., Murphy, T.B., Watson, R.W.G. (2021) Integrating serum biomarkers into prediction models for biochemical recurrence following radical prostatectomy. Cancers, 13(16), 4162. http://dx.doi.org/10.3390/cancers13164162
  31. Frizzarin, M., Gormley, I.C., Berry, D.P., Murphy, T.B., Casa, A., Lynch, A. and McParland, S. (2021). Predicting dairy cow milk quality traits from routinely available milk spectra using statistical machine learning methods. Journal of Dairy Science, 104(7), 7438–7447. http://dx.doi.org/10.3168/jds.2020-19576
  32. Hu, S., Murphy, T.B. and O’Hagan, A. (2021) mvClaim: An R package for multivariate general insurance claims severity modelling. Annals of Actuarial Science, 15(2), 441–457. http://dx.doi.org/10.1017/S1748499521000099
  33. Cappozzo, A., Greselin, F., Duponchel, L. and Murphy, T.B. (2021) Robust variable selection in the framework of classification with label noise and outliers: applications to spectroscopic data in agri-food. Analytica Chemica Acta, 1153, 338245. http://dx.doi.org/10.1016/j.aca.2021.338245
  34. Cappozzo, A., Greselin, F. and Murphy, T.B. (2021) Robust variable selection for model-based learning in presence of adulteration. Computational Statistics & Data Analysis, 158, 107186. http://dx.doi.org/10.1016/j.csda.2021.107186
  35. Jalali, A., Kitching, M., Martin, K., Richardson, C., Murphy, T.B., FitzGerald, S.P., Watson, R.W.G. and Perry, A. (2021) Integrating inflammatory serum biomarkers into a risk calculator for prostate cancer detection. Scientific Reports, 11, 2525. http://dx.doi.org/10.1038/s41598-021-81965-3

  36. Cappozzo, A., Greselin, F. and Murphy, T.B. (2021) Robust semi-supervised learning to discover new wheat varieties and discriminate suspicious kernels in X-ray images. Statistical Learning and Modeling in Data Analysis - Methods and Applications. Balzano, S., Porzio, G.C., Salvatore, R., Vistocco, D. and Vichi, M. (Eds.), Springer. 29–36. http://dx.doi.org/10.1007/978-3-030-69944-4_4

    37. 2020

  37. Ng, T.L.J. and Murphy, T.B. (2020) Model-based clustering of count processes. Journal of Classification, 16, 291–723. http://dx.doi.org/10.1007/s00357-020-09363-4
  38. Cappozzo, A., Greselin, F. and Murphy, T.B. (2020) Anomaly and novelty detection for robust semi-supervised learning. Statistics & Computing, 30, 1545–1571. http://dx.doi.org/10.1007/s11222-020-09959-1
  39. Gilgunn, S., Murphy, K., Stöckmann, H., Conroy, P.J., Murphy, T.B., Watson, R.W., O’Kennedy, R.J., Rudd, P.M. and Saldova, R. (2020) Glycosylation in indolent, significant and aggressive prostate cancer by automated high-throughput N-glycan profiling. International Journal of Molecular Sciences, 21(23), 9233. http://dx.doi.org/10.3390/ijms21239233
  40. Jalali, A., Foley, R.W., Maweni, R.M., Murphy, K., Lundon, D.J., Lynch, T., Power, R., O’Brien, F., O’Malley, K.J., Galvin, D.J., Durkan, G.C., Murphy, T.B., Watson, R.W. (2020) A risk calculator to inform the need for a prostate biopsy: a rapid access clinic cohort. BMC Medical Informatics & Decision Making, 20, 148. http://dx.doi.org/10.1186/s12911-020-01174-2
  41. O’Connor, S., McCaffrey, N., Whyte, E.F., Fop, M., Murphy, T.B. and Moran, K. (2020) Can the Y balance test identify those at risk of contact or non-contact lower extremity injury in adolescent and collegiate Gaelic games? Journal of Science and Medicine in Sport, 23(10), 943–948. http://dx.doi.org/10.1016/j.jsams.2020.04.017
  42. D’Angelo, S., Alfò, M. and Murphy, T.B. (2020) Node-specific effects in latent space modelling of multidimensional networks. Statistica Neerlandica, 74(3), 324–341. http://dx.doi.org/10.1111/stan.12209
  43. Murphy, K. and Murphy, T.B. (2020) Gaussian parsimonious clustering models with covariates and a noise component. Advances in Data Analysis & Classification, 14(2), 293–325. http://dx.doi.org/10.1007/s11634-019-00373-8

  44. Cappozzo, A., Greselin, F. and Murphy, T.B. (2020) A robust approach to model-based classification based on trimming and constraints. Advances in Data Analysis & Classification, 14(2), 327–354. http://dx.doi.org/10.1007/s11634-019-00371-w

    45. 2019

  45. Ng, T.L.J. and Murphy, T.B. (2019) Estimation of the intensity function of an inhomogeneous Poisson process with a change-point. Canadian Journal of Statistics, 47(4), 604–618. http://dx.doi.org/10.1002/cjs.11514
  46. O’Hagan, A., Murphy, T.B., Scrucca, L. and Gormley, I.C. (2019) Investigation of parameter uncertainty in clustering using a Gaussian mixture model via jackknife, bootstrap and weighted likelihood bootstrap. Computational Statistics, 34(4), 1779–1813. http://dx.doi.org/10.1007/s00180-019-00897-9
  47. D’Angelo, S., Murphy, T.B. and Alfò, M. (2019) Latent space modeling of multidimensional networks with application to the exchanges of votes in Eurovision song contest. Annals of Applied Statistics, 13(2), 900–930. http://dx.doi.org/10.1214/18-AOAS1221
  48. Fop, M., Murphy, T.B. and Scrucca, L. (2019) Model-based clustering with sparse covariance matrices. Statistics & Computing, 29(4), 791–819. http://dx.doi.org/10.1007/s11222-018-9838-y
  49. O’Connor, S., McCaffrey, N., Whyte, E.F., Fop, M., Murphy, T.B. and Moran, K. (2019) Is poor hamstring flexibility a risk factor for hamstring injury in Gaelic games? Journal of Sport Rehabilitation, 28(7), 677–681. http://dx.doi.org/10.1123/jsr.2017-0304
  50. Ng, T.L.J. and Murphy, T.B. (2019) Generalized random dot product graph. Statistics & Probability Letters, 148, 143–149. http://dx.doi.org/10.1016/j.spl.2019.01.011
  51. Fosdick, B.K., McCormick, T.H., Murphy, T.B., Ng, T.L.J. and Westling, T. (2019) Multiresolution network models. Journal of Computational and Graphical Statistics, 28(1), 185–196. http://dx.doi.org/10.1080/10618600.2018.1505633

  52. McParland, D. and Murphy, T.B. (2019) Mixture modeling of high-dimensional data. Handbook of Mixture Analysis. G. Celeux, S. Früwirth-Schnatter and C. P. Robert (Eds.), CRC Press. 247–280. http://dx.doi.org/10.1201/9780429055911-11

    53. 2018

  53. Shaikh, M., Antoine, L., Murphy, T.B. and McNicholas, P.D. (2018) Standardizing interestingness measures for association rules. Statistical Analysis and Data Mining, 11(6), 282–295. http://dx.doi.org/10.1002/sam.11394
  54. Murphy, K., Murphy, T.B., Boyce, S., Flynn, L., Gilgunn, S., O’Rourke, C.J., Rooney, C., Stöckmann, H., Walsh, A.L., Finn, S., O’Kennedy, R., O’Leary, J., Pennington, S.R., Perry, A.S., Rudd, P.M., Saldova, R., Shiels, O., Shields, D.C., Watson, R.W. (2018) Integrating biomarkers across omic platforms and biomaterials to stratify patients with indolent and aggressive prostate cancer. Molecular Oncology, 12(9), 1513–1525. http://dx.doi.org/10.1002/1878-0261.12348
  55. Prencipe, M., Fabre, A., Vargyas, E., Murphy, T.B., O’Neill, A., Bjartell, A., Tasken, K.A., Grytli, H., Svindland, A., Berge, V., Eri, L.M., Gallagher, W. and Watson, R.W. (2018) Role of Serum Response Factor expression in prostate cancer biochemical recurrence. The Prostate, 78(10), 724–730. http://dx.doi.org/10.1002/pros.23516
  56. Hu, S., O’Hagan, A. and Murphy, T.B. (2018) Motor insurance claim modeling with factor collapsing and Bayesian model averaging. Stat, 7(1), e180. http://dx.doi.org/10.1002/sta4.180

  57. Fop, M. and Murphy, T.B. (2018) Variable selection methods for model-based clustering. Statistics Surveys, 12, 18–65. http://dx.doi.org/10.1214/18-SS119

    58. 2017

  58. Fop, M., Smart, K. and Murphy, T.B. (2017) Variable selection for latent class analysis with application to low back pain diagnosis. Annals of Applied Statistics, 11(4), 2085–2115. http://dx.doi.org/10.1214/17-AOAS1061

    59. 2016

  59. White, A. and Murphy, T.B. (2016) Exponential family mixed membership models for soft clustering of multivariate data. Advances in Data Analysis & Classification, 10(4), 521–540. http://dx.doi.org/10.1007/s11634-016-0267-5
  60. Scrucca, L., Fop, M., Murphy, T.B. and Raftery, A.E. (2016) mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models. The R Journal, 8(1), 205–233. http://dx.doi.org/10.32614/RJ-2016-021
  61. White, A. and Murphy, T.B. (2016) Mixed-membership of experts stochastic blockmodel. Network Science, 4(1), 48–80. http://dx.doi.org/10.1017/nws.2015.29
  62. Foley, R.W., Maweni, R.M., Gorman, L., Murphy, K., Lundon, D.J., Durkan, G., Power, R., O’Brien, F., O’Malley, K.J., Galvin, D.J., Murphy, T.B. and Watson, R.W. (2016) The ERSPC risk calculators significantly outperform the PCPT 2.0 in the prediction of prostate cancer: a multi-institutional study. BJU International, 118(5), 706–713. http://dx.doi.org/10.1111/bju.13437
  63. White, A., Wyse, J. and Murphy, T.B. (2016) Bayesian variable selection for latent class analysis using a collapsed Gibbs sampler. Statistics & Computing, 26(1), 511–527. http://dx.doi.org/10.1007/s11222-014-9542-5
  64. O’Hagan, A., Murphy, T.B., Gormley, I.C., McNicholas, P. and Karlis, D. (2016) Clustering using the multivariate normal inverse Gaussian distribution. Computational Statistics & Data Analysis, 93(1), 18–30. http://dx.doi.org/10.1016/j.csda.2014.09.006
  65. Gollini, I. and Murphy, T.B. (2016) Joint modelling of multiple network views. Journal of Computational and Graphical Statistics, 25(1), 246–265. http://dx.doi.org/10.1080/10618600.2014.978006

  66. Foley, R.W., Gorman, L.V., Sharifi, N., Lundon, D.J., Murphy, K., Moore, H., Tuzova, A., Perry, A., Murphy, T.B. and Watson, R.W.G. (2016) Improving Multivariable Prostate Cancer Risk Assessment Using The Prostate Health Index. BJU International, 117(3), 409–417. http://dx.doi.org/10.1111/bju.13143

    67. 2015

  67. Bartolucci, F. and Murphy, T.B. (2015) A mixed latent trajectory model for modeling ultrarunners in a 24 hour race. Journal of Quantitative Analysis in Sports, 11(4), 193–204. http://dx.doi.org/10.1515/jqas-2014-0060
  68. Foley, R.W., Lundon, D.J., Murphy, K., Murphy, T.B., O’Malley, K.J. Galvin, D.J. and Watson, R.W.G. (2015) Predicting prostate cancer: analysing the clinical efficacy of prostate cancer risk calculators in a referral population. Irish Journal of Medical Science. 184(3), 701–706. http://dx.doi.org/10.1007/s11845-015-1291-8
  69. Murphy, T.B. (2015) Model-based clustering for network data. Handbook of Cluster Analysis. C. Hennig, M. Meilă, F. Murtagh and R. Rocci (Eds.), CRC Press. 337–357. http://dx.doi.org/10.1201/b19706-22

  70. Salter-Townshend, M. and Murphy, T.B. (2015) Role analysis in networks using a mixture of exponential random graph models. Journal of Computational and Graphical Statistics, 24(2), 520–538. http://dx.doi.org/10.1080/10618600.2014.923777

    71. 2014

  71. McDaid, A., Hurley, N. and Murphy, T.B. (2014) Overlapping stochastic community finding. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), X. Wu, M. Ester and G. Xu (Eds.), IEEE. 17–20. http://dx.doi.org/10.1109/ASONAM.2014.6921554
  72. White, A. and Murphy, T.B. (2014) BayesLCA: An R package for Bayesian latent class analysis. Journal of Statistical Software, 16(13), 1–28. http://dx.doi.org/10.18637/jss.v061.i13
  73. Gormley, I.C. and Murphy, T.B. (2014) Mixed membership models for rank data: Investigating structure in Irish voting data. Handbook of Mixed Membership Models and Their Applications, E. Airoldi, D. Blei, E. Erosheva and S. Fienberg (Eds.), CRC Press. 441–459. http://dx.doi.org/10.1201/b17520-32
  74. Gollini, I. and Murphy, T.B. (2014) Mixture of latent trait analyzers for model-based clustering of categorical data. Statistics & Computing, 24(4), 569–588. http://dx.doi.org/10.1007/s11222-013-9389-1
  75. Caron, F, Teh, Y.W. and Murphy, T.B. (2014) Bayesian nonparametric Plackett-Luce models for the analysis of preferences for college degree programmes. Annals of Applied Statistics, 8(2), 1145–1181. http://dx.doi.org/10.1214/14-AOAS717

  76. Salter-Townshend, M. and Murphy, T.B. (2014) Mixtures of biased sentiment analysers. Advances in Classification and Data Analysis, 8(1), 85–103. http://dx.doi.org/10.1007/s11634-013-0150-6

    77. 2013

  77. Boyce, S., Fan, Y., Watson, R.W.G. and Murphy, T.B. (2013) Evaluation of prediction models for the staging of prostate cancer. BMC Medical Informatics & Decision Making, 13, 126. http://dx.doi.org/10.1186/1472-6947-13-126
  78. Galligan, M., Saldova, R., Campbell, M., Rudd, P.M. and Murphy, T.B. (2013) Greedy feature selection for glycan chromatography data with the generalized Dirichlet distribution. BMC Bioinformatics, 14(1), 155. http://dx.doi.org/10.1007/s11222-013-9389-1
  79. Salter-Townshend, M. and Murphy, T.B. (2013) Sentiment analysis of online media. Algorithms from and for Nature and Life, B. Lausen, D. van del Poel and A. Ultsch (Eds.), Springer. 137–145. http://dx.doi.org/10.1007/978-3-319-00035-0_13
  80. Collins, E.S., Galligan, M.C., Saldova, R., Adamczyk, B., Abrahams, J.L., Campbell, M.P., Ng, C-T, Veale, D.J., Murphy, T.B., Rudd, P.M. and Fitzgerald, O. (2013) Glycosylation status of serum in inflammatory arthritis in response to anti-TNF treatment. Rheumatology, 52(9), 1572–1582. http://dx.doi.org/10.1093/rheumatology/ket189
  81. McDaid, A.F, Murphy, T.B., Friel, N. and Hurley, N. (2013) Improved Bayesian inference for the stochastic block model with application to large networks. Computational Statistics & Data Analysis, 60, 12–31. http://dx.doi.org/10.1016/j.csda.2012.10.021

  82. Salter-Townshend, M. and Murphy, T.B. (2013) Variational Bayesian inference for the latent position cluster model. Computational Statistics & Data Analysis, 57(1), 661–671. http://dx.doi.org/10.1016/j.csda.2012.08.004

    83. 2012

  83. Salter-Townshend, M., White, A., Gollini, I. and Murphy, T.B. (2012) Review of statistical network modelling: Models, algorithms and software. Statistical Analysis & Data Mining, 5(4), 243–264. http://dx.doi.org/10.1002/sam.11146
  84. White, A., Chan, J., Hayes, C. and Murphy, T.B. (2012) Mixed membership models for exploring user roles in online fora. Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media (ICWSM 2012), J. Breslin, N. Ellison, J.G. Shanahan and Z. Tufekci (Eds.), 599–602. http://dx.doi.org/10.1609/icwsm.v6i1.14316
  85. McDaid, A.F, Murphy, T.B., Friel, N. and Hurley, N. (2012) Model-based clustering in networks with Stochastic Community Finding. Proceedings of COMPSTAT 2012: 20th International Conference on Computational Statistics, A. Colubi, K. Fokianos, E.J. Kontoghiorghes and G. Gonzáles-Rodríguez (Eds.), ISI-IASC, 549–560.
  86. O’Hagan, A., Murphy, T.B. and Gormley, I.C. (2012) Computational aspects of fitting mixture models via the Expectation-Maximisation algorithm. Computational Statistics & Data Analysis, 56(12), 3843–3864. http://dx.doi.org/10.1016/j.csda.2012.05.011
  87. Oon, S. F., Fanning, D. M., Fan, Y., Boyce, S., Murphy, T. B., Fitzpatrick, J. M. and Watson, R. W. G. (2012) The identification and validation of a preoperative serum biomarker panel to determine extracapsular extension in patients with prostate cancer. The Prostate, 72(14), 1523–1531. http://dx.doi.org/10.1002/pros.22506
  88. Galvin, R., Lennon, S., Murphy, B.T., Cusack, T., Horgan, F. and Stokes, E. K. (2012) Additional exercise therapy for the recovery of function after stroke (Protocol). Cochrane Database of Systematic Reviews, 6, CD009859. http://dx.doi.org/10.1002/14651858.CD009859

  89. Raza, A., Kennedy, S., Fan, Y., Maher, B., Codd, M., Murphy, T., Wood, A. and Watson, W. (2012) Anti inflammatory effects of statins in cardiac surgery patients. World Journal of Cardiovascular Surgery, 2, 40–47. http://dx.doi.org/10.4236/wjcs.2012.23010

    90. 2011

  90. Toher, D., Downey, G. and Murphy, T.B. (2011) Semi-supervised linear discriminant analysis. Journal of Chemometrics. 25(12), 621–630. http://dx.doi.org/10.1002/cem.1408
  91. Galligan, M., Campbell, M., Saldova, R., Rudd, P. and Murphy, T.B. (2011) Application of compositional models for glycan HILIC data. Proceedings of the 4th International Workshop on Compositional Data Analysis, J.J. Egozcue, R. Tolosana-Delgado. and M.I. Ortego (Eds.). https://congress.cimne.com/codawork11/Admin/Files/FilePaper/p51.pdf
  92. Gormley, I.C. and Murphy, T.B. (2011) Mixture of experts models with social science applications. Mixture Estimation and Applications, K. Mengersen, C. Robert and D.M. Titterington (Eds.), Wiley, 91–110. http://dx.doi.org/10.1002/9781119995678.ch5
  93. Fan, Y., Murphy, T.B., Byrne, J., Brennan, L., Fitzpatrick, J. and Watson, R.W.G. (2011) Applying random forests to identify biomarker panels in serum 2D-DIGE data for the detection and staging of prostate cancer Journal of Proteome Research, 10(3), 1361–1373. http://dx.doi.org/10.1021/pr1011069
  94. Arnold, J.A., Saldova, R., Galligan, M.C., Murphy, T.B., Mimura-Kimura, Y., Telford, J.E., Godwin, A.K. and Rudd, P. M. (2011) Novel glycan biomarkers for the detection of lung cancer. Journal of Proteome Research, 10(4), 1755–1764. http://dx.doi.org/10.1021/pr101034t
  95. Galvin, R., Cusack, T., O’Grady, E., Murphy, T.B. and Stokes, E. (2011) Family mediated exercise intervention [FAME]: evaluation of a novel form of exercise delivery after stroke. Stroke, 42(3), 681–686. http://dx.doi.org/10.1161/STROKEAHA.110.594689

  96. Stokes, E.K, O’Connell, C. and Murphy, T.B. (2011) An investigation into fatigue post stroke and its multidimensional nature. Advances in Physiotherapy. 13(1), 2–10. http://dx.doi.org/10.3109/14038196.2010.534175

    97. 2010

  97. Murphy, T.B., Dean, N. and Raftery, A.E. (2010) Variable selection and updating in model-based discriminant analysis for high dimensional data with food authenticity applications. Annals of Applied Statistics, 4(1), 396–421. http://dx.doi.org/10.1214/09-AOAS279
  98. McNicholas, P.D. and Murphy, T.B. (2010) Model-based clustering of microarray expression data via latent Gaussian mixture models. Bioinformatics, 26(21), 2705–2712. http://dx.doi.org/10.1093/bioinformatics/btq498
  99. Gormley, I.C. and Murphy, T.B. (2010) A mixture of experts latent position cluster model for social network data. Statistical Methodology, 7(3), 385–405. http://dx.doi.org/10.1016/j.stamet.2010.01.002
  100. McNicholas, P.D. and Murphy, T.B. (2010) Model-based clustering of longitudinal data.Canadian Journal of Statistics. 38(1), 153–168. http://dx.doi.org/10.1002/cjs.10047
  101. Gormley, I.C. and Murphy, T.B. (2010) Clustering ranked preference data using sociodemographic covariates. Choice Modelling: The State-of-the-Art and the State-of-Practice, S. Hess and A. Daly (Eds.), Emerald, 543–569. http://dx.doi.org/10.1108/9781849507738-025
  102. McNicholas, P.D., Murphy, T.B., McDaid, A.F. and Frost, D. (2010) Serial and parallel implementations of model-based clustering via parsimonious Gaussian mixture models. Computational Statistics & Data Analysis, 54(3), 711–723. http://dx.doi.org/10.1016/j.csda.2009.02.011
  103. Rowan, F., Docherty, N.G., Murphy, M., Murphy, T.B., Coffey, J.C. and O’Connell, P.R. (2010) Bacterial colonisation of colonic crypts correlates with disease activity in ulcerative colitis. Annals of Surgery, 252(5), 869–875. http://dx.doi.org/10.1097/SLA.0b013e3181fdc54c

  104. Rowan, F., Docherty, N.G., Murphy, M., Murphy, T.B., Coffey, J.C. and O’Connell, P.R. (2010) Desulfovibrio bacterial species are increased in ulcerative colitis. Diseases of the Colon and Rectum, 53(11), 1530–1536. http://dx.doi.org/10.1007/DCR.0b013e3181f1e620

    105. 2009

  105. Gormley, I.C. and Murphy, T.B. (2009) A Grade of Membership model for rank data. Bayesian Analysis, 4(2), 265–296. http://dx.doi.org/10.1214/09-BA410

  106. Fan, Y., Murphy, T.B. and Watson, R.W.G. (2009) digeR: a graphical user interface R package for analyzing 2D DIGE data. Bioinformatics. 25(22), 3033-3034. http://dx.doi.org/10.1093/bioinformatics/btp514

    107. 2008

  107. Gormley, I.C. and Murphy, T.B. (2008) Exploring voting blocs within the Irish electorate: A mixture modeling approach. Journal of the American Statistical Association, 103(483), 1014–1027. http://dx.doi.org/10.1198/016214507000001049
  108. McNicholas, P.D. and Murphy, T.B. (2008) Parsimonious Gaussian mixture models.Statistics & Computing, 18(3), 285–296. http://dx.doi.org/10.1007/s11222-008-9056-0
  109. Gormley, I.C. and Murphy, T.B. (2008) A mixture of experts model for rank data with applications in election studies. Annals of Applied Statistics, 2(4), 1452–1477.http://dx.doi.org/10.1214/08-AOAS178
  110. McNicholas, P., Murphy, T.B. and O’Regan, M. (2008) Standardising the lift of an association rule. Computational Statistics & Data Analysis, 52(10), 4712–4721. http://dx.doi.org/10.1016/j.csda.2008.03.013
  111. Coote, S., Murphy, T.B., Harwin, W. and Stokes, E.K. (2008) The effect of the GENTLE/s robot-mediated therapy system on arm function after stroke. Clinical Rehabilitation,22(5), 395–405. http://dx.doi.org/10.1177/0269215507085060

  112. Galvin, R., Murphy, T.B., Cusack, T. and Stokes, E.K. (2008) The impact of increased duration of exercise therapy on functional recovery following stroke - what is the evidence? Topics in Stroke Rehabilitation, 15(4), 365–377. http://dx.doi.org/10.1310/tsr1504-365

    113. 2007

  113. Gormley, I.C. and Murphy, T.B. (2007) A latent space model for rank data. Statistical Network Analysis: Models, Issues and New Directions. E. Airoldi, D. M. Blei, S. E. Fienberg, A. Goldenberg, E. P. Xing and A. X. Zheng (Eds.), Lecture Notes in Computer Science, Volume 4503, Springer-Verlag, Berlin, 90–102. http://dx.doi.org/10.1007/978-3-540-73133-7_7
  114. Toher, D., Downey, G. and Murphy, T.B. (2007) A comparison of model-based and regression classification techniques applied to near infrared spectroscopic data in food authentication studies. Chemometrics and Intelligent Laboratory Systems, 89(2), 102–115. http://dx.doi.org/10.1016/j.chemolab.2007.06.005
  115. Gormley, I.C. and Murphy, T.B. (2007) Expectation Maximization algorithm. Encyclopedia of Statistics in Quality and Reliability. F.Ruggeri, R. Kennett and F. W. Faltin (Eds.), Wiley, Chichester, 621–626. http://dx.doi.org/10.1002/9780470061572.eqr480

  116. Kennedy, N., Stokes, E., O’Shea, E., Murphy, T.B., Bresnihan, B., FitzGerald, O. (2007) Inpatient and outpatient rehabilitation for patients with rheumatoid arthritis: A clinical and economic assessment. Journal of Medical Economics, 10(4), 515–528. http://dx.doi.org/10.3111/13696990701725850

    117. 2006

  117. Dean, N., Murphy, T.B. and Downey, G. (2006) Using unlabelled data to update classification rules with applications in food authenticity studies. Journal of the Royal Statistical Society, Series C: Applied Statistics. 55(1), 1–14. http://dx.doi.org/10.1111/j.1467-9876.2005.00526.x

  118. Gormley, I.C. and Murphy, T.B. (2006) Analysis of Irish third-level college applications data.Journal of the Royal Statistical Society, Series A: Statistics in Society. 169(2), 361–380. http://dx.doi.org/10.1111/j.1467-985X.2006.00412.x

    119. 2003

  119. Murphy, T.B. and Martin, D. (2003) Mixtures of distance-based models for ranking data. Computational Statistics & Data Analysis. 41 (3-4), 645–655. http://dx.doi.org/10.1016/S0167-9473(02)00165-2

    120. 2002

  120. Hartigan, J.A. and Murphy, T.B. (2002) Inferred probabilities. Journal of Statistical Planning and Inference. 105(1), 23–34. http://dx.doi.org/10.1016/S0378-3758(01)00202-6

  121. Murphy, T.B. (2002) Bayesian robustness for multivariate problems. Distributions with Given Marginals and Statistical Modelling. C.Cuadras, J. Fortiana, and J. Rodriguez Lallena (Eds.) Kluwer, Boston. 161–168. http://dx.doi.org/10.1007/978-94-017-0061-0_17