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Department of Statistics

Publications

Recent Reports

  1. C. Kimchaiwong, J. Houssineau and A. M. Johansen. Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty. arXiv e-print 2411.18864, 2024. [arxiv]
  2. F. R. Crucinio and A. M. Johansen. Solving Fredholm Integral Equations of the Second Kind using Wasserstein gradient flows. arXiv e-print 2409.19642, 2024. [arxiv]
  3. R. Caprio and A. M. Johansen. Fast convergence of the Expectation Maximization algorithm under a logarithmic Sobolev inequality. arXiv e-print 2407.17949, 2024. [arxiv]
  4. J. Koskela, P. Jenkins, A. M. Johansen and D. Spanò. Genealogical processes of non-neutral population models under rapid mixing. arXiv e-print 2406.16465, 2024. [arxiv]
  5. R. Caprio, J. Kuntz, S. Powers, and A. M. Johansen. Non-asymptotic error bounds for particle gradient descent and related functional inequalities. arXiv e-print 2403.02004, 2024. [arxiv]
  6. R. Salomone, L. F. South, A. M. Johansen, C. C. Drovandi, and D. P. Kroese. Unbiased and consistent nested sampling via sequential Monte Carlo. arXiv e-print 1805.03924, 2023. [arxiv]
  7. R. Caprio and A. M. Johansen. A calculus for Markov chain Monte Carlo: studying approximations in algorithms. arXiv e-print 2310.03853, 2023. [arxiv]
  8. A. Finke, A. Doucet and A. M. Johansen. On embedded hidden Markov models and particle Markov chain Monte Carlo methods. arXiv e-print 1610.08962 [arxiv]

Journal Articles

  1. F. R. Crucinio, V. De Bortoli, A. Doucet, and A. M. Johansen. Solving a class of Fredholm integral equations of the first kind via Wasserstein gradient flows. Stochastic Processes and their Applications, 173:104374, 2024. [journal|arxiv]
  2. J. Kuntz, F. R. Crucinio, and A. M. Johansen. Divide-and-conquer Sequential Monte Carlo: Theoretical properties and limit theorems. Annals of Applied Probability, 34(1B):1469-1523. 2024. [arxiv|journal]
  3. F. R. Crucinio and A. M. Johansen. A divide-and-conquer sequential Monte Carlo approach to high-dimensional filtering, Statistica Sinica, 34: 1093-1113. 2024. [journal|arxiv]
  4. A. Viani, A. M. Johansen and A. Sorrentino. Cost-free hyper-parameter selection/averaging for Bayesian inverse problems with vanilla and Rao–Blackwellized SMC samplers. Statistics and Computing, 33(126):1-15, 2023. [journal|arxiv]
  5. F. R. Crucinio and A. M. Johansen. Properties of marginal sequential Monte Carlo. Statistics and Probability Letters, 203:109914, 2023. [journal|arxiv]
  6. R. Chan, M. Pollock, A. M. Johansen, and G. O. Roberts. Divide-and-conquer Monte Carlo Fusion. Journal of Machine Learning Research, 174(193):1-82, 2023. [journal|arxiv]
  7. F. R. Crucinio, A. Doucet, and A. M. Johansen. A particle method for solving Fredholm integral equations of the first kind. Journal of the American Statistical Association, 118:542, 937-947, 2023. [journal|arxiv]
  8. S. Brown and P. A. Jenkins and A. M. Johansen and J. Koskela. Weak Convergence of Non-neutral Genealogies to Kingman's Coalescent. Stochastic Processes and their Applications,162:76-105, 2023. [journal|arxiv]
  9. J. Hodgson, A. M. Johansen, and M. Pollock. Unbiased simulation of rare events in continuous time. Methodology and Computing in Applied Probability, 24:2123-2148, 2022. [journal|arxiv]
  10. D. Thesingarajah and A. M. Johansen. The Node-wise Pseudo-marginal Method. Statistics and Computing, 32(43) 1-31: 2022. [journal|arxiv]
  11. J. Kuntz, F. R. Crucinio, and A. M. Johansen. Product-form estimators: exploiting independence to scale up Monte Carlo. Statistics and Computing, 32(12):1–22, 2022. [journal|arxiv]
  12. M. Unosson, M. Brancaccio, M. Hastings, A. M. Johansen, and B. Finkenstädt. A spatio-temporal model to reveal oscillator phenotypes in gene expression with application to circadian rhythms in the SCN. PLOS Computational Biology 17(12): e1009698, 2021. [journal|bioRxiv]
  13. L. Angeli, S. Grosskinsky, A. M. Johansen. Limit theorems for cloning algorithms. Stochastic Processes and their Applications 138:117–152, 2021. [journal|arxiv]
  14. L. J. Rendell, A. M. Johansen, A. Lee and N. Whiteley. Global consensus Monte Carlo. Journal of Computational and Graphical Statistics 30(2):249–259, 2021. [journal|arxiv]
  15. S. Brown and P. A. Jenkins and A. M. Johansen and J. Koskela. Simple conditions for convergence of sequential Monte Carlo genealogies with applications. Electronic Journal of Probability 26(1):1–22, 2021. [journal|arxiv]
  16. M. Pollock, P. Fearnhead, A. M. Johansen and G. O. Roberts. Quasi-stationary Monte Carlo methods and the ScaLE algorithm.Journal of the Royal Statistical Society Series B (Statistical Methodology) 82(5):1167–1221, 2020. [journal|arxiv]
  17. A. Finke, A. Doucet, and A. M. Johansen. Limit theorems for sequential MCMC methods. Advances in Applied Probability 52(2):377-403, 2020 [journal|arxiv] Selected as part of the APT's December 2024 Collection on Markov chain Monte Carlo Methods.
  18. J. Koskela, P. Jenkins, A. M. Johansen, and D. Spanò. Asymptotic genealogies of interacting particle systems with an application to sequential Monte Carlo. Annals of Statistics 48(1):560–583, 2020 as corrected in Erratum: Asymptotic genealogies of interacting particle systems with an application to sequential Monte Carlo. Annals of Statistics, 50(4):2467 – 2468, 2022. [journal|arxiv]
  19. L. Angeli, S. Grosskinsky, A. M. Johansen and A. Pizzoferrato. Rare event simulation for stochastic dynamics in continuous time. Journal of Statistical Physics 176:1185–1210, 2019. [journal|arxiv]
  20. M. Thorpe and A. M. Johansen. Pointwise Convergence in Probability of General Smoothing Splines. Annals of the Institute of Statistical Mathematics 70(4):717–744, 2018. [journal|arxiv]
  21. P. Guarniero, A. M. Johansen and A. Lee. The Iterated Auxiliary Particle Filter. Journal of the American Statistical Association 112(520):1636–1647, 2017 [journal|arxiv]
  22. Faye M. Nixon, Thomas R. Honnor, Nicholas I. Clarke, Georgina P. Starling, Alison J. Beckett, Adam M. Johansen, Julia A. Brettschneider, Ian A. Prior, Stephen J. Royle. Microtubule organization within mitotic spindles revealed by serial block face scanning EM and image analysis. Journal of Cell Science 130:1845–1855, 2017 [journal]
  23. F. Lindsten, A. M. Johansen, C. Naesseth, B. Kirkpatrick, T. Schön, J. A. D. Aston, and A. Bouchard-Côté. Divide and conquer with sequential Monte Carlo. Journal of Computational and Graphical Statistics 26(2):445–458, 2017. [journal website|arxiv]
  24. R. G. Everitt, A. M. Johansen, E. Rowing, and M. Evdemon-Hogan. Bayesian model selection with un-normalised likelihoods. Statistics and Computing 27(2):403–422, 2017. [journal|arxiv].
  25. M. Thorpe and A. M. Johansen. Convergence and Rates for Fixed-Interval Multiple-Track Smoothing Using k-Means Type Optimization. Electronic Journal of Statistics 10(2):3693–3722, 2016. [journal|arxiv]
  26. Y. Zhou, A. M. Johansen and J. A. D. Aston, Towards Automatic Model Comparison: An Adaptive Sequential Monte Carlo Approach. Journal of Computational and Graphical Statistics, 25(3):701–726, 2016. [journal|arxiv]
  27. M. Pollock, A. M. Johansen and G. O. Roberts, On Exact and $\varepsilon$-strong Simulation of (Jump) Diffusions. Bernoulli, 22(2):794–856, 2016. [pdf|journal website|arxiv].
  28. M. Thorpe, F. Theil, A. M. Johansen, and N. Cade. Convergence of the k-means minimization problem using $\Gamma$-convergence. SIAM Journal on Applied Mathematics, 75(6):2444–2474, 2015. [journal||arxiv]
  29. N. Barry, A. Pitto-Barry, J. Tran, S. Spencer, A. M. Johansen, A. M. Sanchez, A. P. Dove, R. K. O'Reilly, R. Deeth, R. Beanland, P. J. Sadler. Osmium Atoms and Os2 Molecules Move Faster on Selenium-doped Compared to Sulfur-doped Boronic Graphenic Surfaces, Chemistry of Materials, 27(14):5100–5106, 2015. [ journal ]
  30. A. Finke, A. M. Johansen and D. Spanò, Static-parameter estimation in piecewise deterministic processes using particle Gibbs samplers. Annals of the Institute of Statistical Mathematics (Tokyo), 66(3):577–609, 2014. [Journal Version | Preprint also available as CRiSM Working Paper 14-03]
  31. C. F. H. Nam, J. A. D. Aston and A. M. Johansen. Parallel Sequential Monte Carlo Samplers and Estimation of the Number of States in a Hidden Markov Model. Annals of the Institute of Statistical Mathematics (Tokyo), 66(3):553–575, 2014. [Journal version | CRiSM Working Paper 12-23 (earlier version)]
  32. A. Sorrentino, A. M. Johansen, J. A. D. Aston, T. E. Nichols and W. S. Kendall. Dynamic filtering of Static Dipoles in MagnetoEncephaloGraphy. Annals of Applied Statistics, 7(2):955–988, 2013. [Journal Version | arxiv]
  33. F. J. Rubio and A. M. Johansen, A simple approach to maximum intractable likelihood estimation. Electronic Journal of Statistics, 7:1632–1654, 2013. [Journal version]
    See also F. J. Rubio's Vignette at RPubs
  34. Y. Zhou, J. A. D. Aston and A. M. Johansen, Bayesian Model Comparison for Compartmental Models with Applications in Positron Emission Tomography. Journal of Applied Statistics, 40(5):993–1016, 2013. [Journal version]
  35. C. F. H. Nam, J. A. D. Aston and A. M. Johansen, Quantifying the Uncertainty in Change Points. Journal of Time Series Analysis, 33(5):807–823, 2012 [Journal]
  36. N. Whiteley, A. M. Johansen, and S. Godsill. Monte Carlo filtering of piecewise-deterministic processes. Journal of Computational and Graphical Statistics, 20(1):119–139, 2011. [Journal Version|.pdf]
  37. X. Didelot, R. G. Everitt, A. M. Johansen, and D. J. Lawson. Likelihood-free estimation of model evidence. Bayesian Analysis, 6(1):49–74, March 2011. [Journal]
  38. A. Doucet, A. M. Johansen, and V. B. Tadić. On solving integral equations using Markov Chain Monte Carlo. Applied Mathematics and Computation, 216:2869–2880, 2010. [Journal Version|.pdf ]
  39. A. M. Johansen. SMCTC: Sequential Monte Carlo in C++. Journal of Statistical Software, 30(6):1–41, April 2009. [Journal Version|Draft with line numbers for source code.]
  40. A. M. Johansen and A. Doucet. A note on the auxiliary particle filter. Statistics and Probability Letters, 78(12):1498–-1504, September 2008. [.djvu | http | .ps | .pdf ]
  41. A. M. Johansen, A. Doucet, and M. Davy. Particle methods for maximum likelihood parameter estimation in latent variable models. Statistics and Computing, 18(1):47–57, March 2008. [Journal|.pdf ]
  42. J. R. James, S. S. White, R. W. Clarke, A. M. Johansen, et al. Single molecule-level analysis of the subunit composition of the T-cell receptor on live T cells. Proceedings of the National Academy of Science, USA, 104(45):17662–17667, November 2007. [Journal ]
  43. G. W. Peters, A. M. Johansen, and A. Doucet. Simulation of the annual loss distribution in operational risk via Panjer recursions and Volterra integral equations for value at risk and expected shortfall estimation. Journal of Operational Risk, 2(3):29–58, Fall 2007. [ http ]
  44. A. M. Johansen, S. S. Singh, A. Doucet, and B.-N. Vo. Convergence of the SMC implementation of the PHD filter. Methodology and Computing in Applied Probability, 8(2):265–291, June 2006. [http | .pdf ]
  45. Jodie Smith, David Onley, Caroline Garey, Stuart Crowther, Nicholas Cahir, Adam Johansen, Sianie Painter, Grant Harradence, Ricardo Davis, and Peter Swarbrick. Determination of ANA specificity using the UltraPlexTM platform. Annals of the New York Academy of Sciences, 1050:286–294, 2005. [Journal]
  46. C. J. Edgcombe and A. M. Johansen. Current-voltage characteristics of nonplanar cold field emitters. Journal of Vacuum Science Technology B, 21(4):1519–1523, July 2003. [journal | .djvu | .ps | .pdf ]

Conference Proceedings

  1. J. N. Lim and A. M. Johansen. Particle semi-implicit variational inference. To appear in Proceedings of Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), 2024. [arxiv]
  2. J. N. Lim, J. Kuntz, S. Power, and A. M. Johansen. Momentum particle maximum likelihood. In Proceedings of 41st International Conference on Machine Learning (ICML), Vienna, Austria, PMLR 235:29816-29871, 2024. [proceedings|arxiv]
  3. J. Kuntz and J. N. Lim A. M. Johansen. Particle algorithms for maximum likelihood training of latent variable models. In Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:5134-5180, 2023. (oral presentation). [proceedings|arxiv]
  4. A. Boustati, Ö. D. Akylidìz, T. Damoulas, and A. M. Johansen. Generalized Bayesian filtering via sequential Monte Carlo. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 418–429. Curran Associates, Inc., 2020. [NeurIPS Proceedings|NeurIPS Supplement|arxiv (earlier version)]
  5. A. M. Johansen. On Blocks, Tempering and Particle MCMC for Systems Identification. In Proceedings of 17th IFAC Symposium on System Identification, October 19-21, 2015, p.969-974, Beijing International Convention Center, Beijing, China. [.pdf | proceedings]
  6. Y. Zhou, A. M. Johansen and J. A. D. Aston, Bayesian model comparison via path-sampling sequential Monte Carlo. In Proceedings of IEEE Workshop on Statistical Signal Processing, 2012. [Abstract|pdf]
  7. A. M. Johansen, N. Whiteley and A. Doucet. Exact approximation of Rao-Blackwellised particle filters. In Proceedings of 16th IFAC Symposium on Systems Identification. Brussels, July 2012. [pdf]
  8. A. M. Johansen and N. Whiteley. A modern perspective on auxiliary particle filters. In Proceedings of Workshop on Inference and Estimation in Probabilistic Time Series Models. Isaac Newton Institute, June 2008. [.pdf ]
  9. N. Whiteley, A. M. Johansen, and S. Godsill. Efficient Monte Carlo filtering for discretely observed jumping processes. In Proceedings of IEEE Statistical Signal Processing Workshop, pages 89-93, Madison, WI, USA, August 26th-29th 2007. [ .djvu | .ps | .pdf ]
  10. A. M. Johansen, P. Del Moral, and A. Doucet. Sequential Monte Carlo samplers for rare events. In Proceedings of the 6th International Workshop on Rare Event Simulation, pages 256-267, Bamberg, Germany, October 2006. [.djvu | .ps | .pdf ]
  11. A. M. Johansen, A. Doucet, and M. Davy. Maximum likelihood parameter estimation for latent models using sequential Monte Carlo. In Proceedings of ICASSP, volume III, pages 640-643, May 2006. [.djvu | .ps | .pdf ]

Book Chapters

  1. A. M. Johansen. Sequential Monte Carlo: Particle filtering and beyond. In W. W. Piegorsch, R. Levine, H. H. Zhang, and T. C. M. Lee, editors, Computational Statistics in Data Science. Wiley, 2021. [chapter]
  2. A. M. Johansen. Particle Filtering. In Wiley StatsRef: Statistics Reference Online, May 2019. Wiley.
  3. A. M. Johansen. Markov chain Monte Carlo. In SAGE Encyclopedia of Educational Research, Measurement and Evaluation, February 2018. SAGE.
  4. J. A. D. Aston and A. M. Johansen. Bayesian Inference on the Brain: Bayesian Solutions to Selected Problems in Neuroimaging. In Current Trends in Bayesian Methodology, May 2015. CRC Press.
  5. N. Whiteley and A. M. Johansen. Auxiliary Particle Filtering: Recent Developments. In Bayesian Time Series Models, 2011, Barber, Cemgil and Chiappa (eds). Cambridge University Press, [pdf]
  6. A. Doucet and A. M. Johansen. A Tutorial on Particle filtering and smoothing: Fiteen years later. In The Oxford Handbook of Nonlinear Filtering, D. Crisan and B. Rozovsky (eds.). Oxford University Press, 2011. [preprint]
  7. A. M. Johansen. Monte Carlo methods. In E. Baker, P. Peterson and B. McGraw, editors, International Encyclopaedia of Education. Elsevier, 3rd edition, 2010.
  8. A. M. Johansen. Markov Chain Monte Carlo. In E. Baker, P. Peterson and B. McGraw, editors, International Encyclopaedia of Education. Elsevier, 3rd edition, 2010.
  9. A. M. Johansen. Markov Chains. In B. Wah, editor, Encyclopaedia of Computer Science and Engineering. John Wiley and Sons, Inc., 111 River Street, MS 8-02, Hoboken, NJ 07030-5774, Volume 4:1800-1808, January 2009. [ http | Wiley Encyclopedia of Computer Science and Engineering at Amazon.co.uk]

Discussions

  1. A. Finke, A. Hetland, A. Lee and A. M. Johansen. Discussion of "Sequential Quasi-Monte Carlo Methods" by Gerber and Chopin Journal of the Royal Statistical Society B, 77(3):557–558, 2015.
  2. M. Pollock, A. M. Johansen, K. Łatuszýnski and G. O. Roberts. Discussion of "Sequential Quasi-Monte Carlo Methods" by Gerber and Chopin. Journal of the Royal Statistical Society B, 77(3):556–557, 2015.
  3. A. Doucet, P. Jacob and A. M. Johansen, Discussion of "Riemannian Manifold Langevin and Hamiltonian Monte Carlo Methods" by Girolami and Calderhead. Journal of the Royal Statistical Society B, 73(2):162 April 2011.
  4. A. M. Johansen and J. A. D. Aston, Discussion of "Particle Markov chain Monte Carlo methods" by Andrieu, Doucet and Holenstein. Journal of the Royal Statistical Society B, 72(3):326-327, June 2010.
  5. A. M. Johansen, Discussion of "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations" by Rue, Martino, and Chopin. Journal of the Royal Statistical Society B, 71(2):358, April 2009.

Older Reports & Miscellanea

  1. A. M. Johansen and A. Doucet. Auxiliary variable sequential Monte Carlo methods. Research Report 07:09, University of Bristol, Department of Mathematics - Statistics Group, University Walk, Bristol, BS8 1TW, UK, July 2007. [.djvu | .ps | .pdf ]
    A substantially shorter version focusing on particle filtering subsequently appeared in Statistics and Probability Letters.
  2. A. M. Johansen. Some Non-Standard Sequential Monte Carlo Methods With Applications. PhD thesis, University of Cambridge Department of Engineering, 2006. [.pdf ]
  3. Adam Michael Johansen and W. J. Fitzgerald. Unsupervised generalised Gaussian mixture model classification using the EM algorithm. Technical Report CUED/F-INFENG/TR-455, University of Cambridge, Department of Engineering, Cambridge University Engineering Department, Trumpington Street, Cambridge, CB2 1PZ, May 2003.