Dr Adam Johansen is a Reader in Statistics; his research focuses upon methodological and theoretical aspects of simulation-based algorithms.
He is a group leader within the Data Centric Engineering Programme of The Alan Turing Institute: see the project page for more details and get in touch if you're interesting in becoming involved.
He leads the Robust, Scalable Sequential Monte Carlo with Application To Urban Air Quality project — again, get in touch if you're interested in becoming involved.
He is an investigator within The CoSinES Project.
He is co-director of APTS.
Some generic teaching information - - applicable to my personal tutees, MSc students and those attending my lectures is available from my teaching page.
Students and Collaborators
Current interests include Monte Carlo methodology, particularly sequential methods together with Bayesian statistics and decision theory more generally.
Prospective Ph.D. students should feel free to email me to discuss possible research directions and might find the theses of some of my former students (available by following the above link) useful indicators of the types of project in which I am typically involved.
Current Ph.D. Students:
- Letizia Angeli (co-supevisor Stefan Grosskinsky)
- Susanna Brown (co-supervisor Jere Koskela)
- Francesca Crucinio (co-supervisor Arnaud Doucet)
- James Hodgson (co-supervisor Murray Pollock)
- Denishrouf Thesingarajah
- Måns Unosson (co-supervisor Bärbel Finkenstädt )
(Pre)Publications to date are listed here. Selected recent additions are listed below.
- M. Pollock, P. Fearnhead, A. M. Johansen and G. O. Roberts. Quasi-stationary Monte Carlo methods and the ScaLE algorithm.To appear with discussion in Journal of the Royal Statistical Society Series B (Statistical Methodology) [journal preprint | arxiv]
- A. Finke, A. Doucet, and A. M. Johansen. Limit theorems for sequential MCMC methods. To appear in Advances in Applied Probability 52(2) [arxiv]
- 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. [journal|arxiv] The journal has unfortunately printed an earlier version which contains an error in Case 1 of Lemma 1 which is corrected in the arxiv version.
- 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]
- 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]
- 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]
- M. Pollock, A. M. Johansen and G. O. Roberts, On Exact and -strong Simulation of (Jump) Diffusions. Bernoulli, 22(2):794–856, 2016. [pdf|journal website|arxiv].
- SMCTC: A Sequential Monte Carlo Template Class (C++)
- RcppSMC: An Rcpp library which has evolved from the above (currently version 0.2.1) ; the development version of RcppSMC lives on github and a google-groups-based discussion list also exists.
Adam M. Johansen
a dot m dot johansen at warwick.ac.uk