Ajay Jasra


Associate Professor, Department of Statistics and Applied Probability (DSAP), National University of Singapore.


Degrees, Positions & Editorial Work


Since 12/2011-: Associate Professor (Tenure Track), DSAP, National University of Singapore, SG.

09/2008-12/2011: Assistant Professor (Tenured), Mathematics, Imperial College London.

03/2008-09/2008: Lecturer, Institute of Statistical Mathematics, JP.

10/2006-03/2008: Chapman Fellow of Mathematics, Mathematics, Imperial College London.

02/2006-10/2006: Research Associate, Engineering, University of Cambridge.

10/2005-02/2006: Research Associate, Statistics, University of Oxford.

      10/2002-10/2005: Ph.D., Imperial College London.


I am also an affiliated member of the risk management institute and centre for quantitative

finance at NUS. I am an associate member of the stochastic analysis group at Imperial College London.


I am currently associate editor at:


American Journal of Algorithms and Computing (2012-)

Statistics and Computing (2011-)

Stat (2012-).




My Research is funded by the following grants:


AcRF Tier 2 Grant: `Approximate Computational Methods for High-Dimensional Systems’ (Co-PI with Nott).

2014-2017, S$572820.

EPSRC Grant: `Advanced Stochastic Computation for Stochastic Trees’ (Visiting Researcher with De Iorio,

Beskos and Balding). 2013-2016, £401382.

MOE Grant: `Approximate and High-Dimensional Filtering’. 2012-2015, S$174200.


Research Interests


Bayesian Statistics.


Computational finance.


Markov chain theory.


Monte Carlo methods.


For more information on my research, please see my Google Scholar profile.


Publications: Journals (40)


(2014) Persing A. & Jasra A. Twisting the alive particle filter. Meth. Comp. Appl. Probab. (to appear). arXiv.


(2014) Wang J. & Jasra A. Monte Carlo algorithms for computing alpha-permanents. Stat. Comp. (to appear). Preliminary.


(2014) Yildirim, S., Singh S. S., Dean T., & Jasra A. Parameter estimation in HMMs with intractable likelihoods via SMC. JCGS (to appear). arXiv.


(2014) Kantas, N., Beskos, A., & Jasra, A. SMC for inverse problems: a case study for Navier Stokes. SIAM/ASA JUQ (to appear). arXiv.


(2014) Jasra, A. On the behaviour of the backward Feynman-Kac formula. J. Appl. Probab. (to appear). arXiv.


(2014) Dean, T., Singh, S., Jasra, A. & Peters, G. Estimation of HMMs with Intractable Likelihoods. Scand. J. Statist. (to appear). arXiv.


(2014) Zhang, X., Nott, D., Yau, C. & Jasra, A. A sequential algorithm for fast fitting of mixtures. JCGS (to appear). arXiv.


(2014) Ehrlich, E., Jasra, A., & Kantas, N. Gradient free parameter estimation for intractable HMMs. Meth. Comp. Appl. Probab. (to appear). arXiv.


(2014) Cozzini, A., Jasra, A. Montana G. & Persing A. Bayesian Mixture of Lasso Regressions., Comput. Stat. Data Anal. 77, 84-97. arXiv.


(2014) Beskos, A., Crisan, D. & Jasra, A. On the stability of SMC in high-dimensions. Ann. Appl. Probab. 24, 1396-1445. arXiv.


(2014) Jasra, A., Kantas, N. & Ehrlich E. Approximate inference for observation driven time series. TOMACS, 24, article 13. arXiv.


(2014) Martin, J., Jasra, A., Singh, S., Whiteley, N., Del Moral, P. & McCoy, E. ABC Smoothing. Stoch. Anal. Appl. 32, 397-422. arXiv.


(2014) Beskos, A., Crisan D., Jasra, A. & Whiteley, N. Error bounds for SMC in high-dimensions. Adv. Appl. Probab. 46, 279-306. arXiv.


(2014) Wang J., Jasra A. & De Iorio M. Computational methods for network models. J. Comp. Biol. 21, 141-161. arXiv.


(2014) Jasra, A., Kantas N. & Persing, A. Bayesian inference for partially observed stopped processes Stat. Comp. 24, 1-20. arXiv.


(2013) Cozzini, A., Jasra, A. & Montana, G. Model-based clustering with gene ranking. J. Bio. Comp. Biol. 11, 1341007. arXiv.


(2013) Martin, J., Jasra, A. & McCoy, E. Inference for a class of partially observed point process. Ann. Inst. Statist. Math. 65, 413-437. arXiv.


(2013) Persing A. & Jasra A. Likelihood estimation for hidden Markov models, Stat. Prob. Lett. 83, 1433-1442. Preliminary.


(2012) Tsagaris, T, Jasra, A & Adams, N. Robust and Adaptive algorithms for online portfolio selection. Quant. Finance. 12, 1651-1662. arXiv.


(2012) Jasra, A., Singh, S, Martin, J & McCoy E. Filtering via ABC, Stat. Comp. 22, 1223-1237. (in the special issue on ABC: introduction).


(2012) Del Moral, P., Doucet, A. & Jasra, A. An Adaptive SMC Method for Approximate Bayesian Computation. Stat. Comp. 22, 1009-1020.


(2012) Whiteley, N., Kantas, N. & Jasra, A. Linear variance for particle approximations of F-K formulae. Stoch. Proc. Appl. 122, 1840-1865.


(2012) Del Moral, P., Doucet, A. & Jasra, A. On Adaptive Resampling Procedures for SMC Methods, Bernoulli. 18, 252-272.


(2011) Jasra, A, De Iorio, M. & Chadeau-Hyam M The Time Machine. Proc. Roy. Soc. A. 467, 2350-2370. arXiv.


(2011) Andrieu, C, Jasra, A, Doucet, A & Del Moral, P, On Non-Linear Markov chain Monte Carlo, Bernoulli. 17, 987-1014.


(2011) Jasra, A. & Holmes CC. Stochastic Boosting Algorithms, Stat. Comp. 21, 335-347.


(2011) Jasra, A. & Del Moral, P. SMC for option pricing, Stoch. Anal. Appl. 29, 292-316.


(2011) Jasra, A. Stephens, DA, Doucet, A, & Tsagaris, T., Inference for Levy driven SV Models, Scand. J. Statist. 38, 1-22.


(2009) Jasra, A. & Doucet, A. SMC Methods for Diffusions, Proc. Roy. Soc. A. 465, 3709-3727.


(2009) Holmes, CC & Jasra, A. Antithetic Methods for Gibbs Samplers, JCGS. 18, 401-414.


(2009) Pei, T., Jasra, A., Hand D. J., Zhu, A. & Zhou, C. DECODE: a new method for clusters in spatial data, Data Min. Know. Disc. 18, 337-369.


(2008) Jasra, A, & Doucet, A, Stability of SMC Samplers. Stat. Prob. Lett. 78, 3062-3069.


(2008) Andrieu, C, Jasra, A, Doucet, A & Del Moral, P, A note on the convergence of the Equi-Energy Sampler, Stoch. Anal. Appl. 26, 298-313.


(2008) Jasra, A, Doucet, A, Stephens, DA & Holmes, CC, Interacting SMC Samplers, Comput. Stat. Data Anal. 10, 1765-1791.


(2008) Jasra, A, & Yang, C, A regeneration proof of the CLT for uniformly ergodic Markov chains, Stat. Prob. Lett. 78, 1649-1655.


(2007) Jasra, A, Stephens, DA, & Holmes, CC Population-based reversible Jump MCMC, Biometrika. 94, 787-807.


(2007) Jasra, A, Stephens DA & Holmes, CC, On population-based simulation for static inferenceStat. Comp. 17, 263-279.


(2006) Jasra, A, Stephens, DA, Gallagher, KL & Holmes, CC, Bayesian Mixture Modelling in Geochronology via MCMC, Math. Geol. 38, 269-300.


(2006) Del Moral, P, Doucet, A & Jasra, A, Sequential Monte Carlo Samplers, JRSSB. 68, 411-436. A related note is available here.


(2005) Jasra A, Holmes CC & Stephens DA, MCMC methods and the label switching problem in Bayesian mixture modelling, Stat. Sci. 20, 50-67.


Publications: Conference/ In Books/ Discussions/Book Reviews (11)


(2012) Jasra, A. Review of `Understanding Probability’. Asia Pacific Mathematics News Letter.


(2012) Barthelme’ S., Chopin, N., Jasra A. & Singh, S. Comments on the paper of Fearnhead & Prangle.

(Thanks to Christian Robert for producing the linked document), JRSSB.


(2012) Jasra, A. Comments on the paper of Fearnhead & Prangle. JRSSB.


(2012) Jasra, A. Review of `Exploring Monte Carlo Methods’. Materials Today.


(2011) Jasra, A. & Singh, S. Comments on the paper of Girolami & Calderhead, JRSSB.


(2007) Hoffman, M., Doucet, A. De Freitas, N. & Jasra, A. Bayesian Policy Learning with Trans-Dimensional MCMC, NIPS.


(2007) Andrieu, C, Jasra, A, Doucet, A & Del Moral, P, Convergence of the Equi-Energy Sampler,



(2007) Andrieu, C, Jasra, A, Doucet, A & Del Moral, P, Non-Linear Markov chain Monte Carlo,



(2006) Gallagher, KL, Jasra, A, Stephens, DA & Holmes, CC, A new approach to mixture modelling for geochronology,

16th Annual V M Goldschmidt Conference.


(2006) Doucet, A, Montesano, L & Jasra, A, Optimal filtering for partially observed point processes, ICASSP.


(2006) Del Moral, P, Doucet, A & Jasra, A, Sequential Monte Carlo for Bayesian Computation, Bayesian Statistics 8.



Technical Reports (7)


These are all submitted (or about to be) preprints.


(2014) Persing, A., Jasra A., Beskos A., Balding D. & De Iorio M. A simulation approach for change-points on trees.


(2014) Wang, S., Shi, T., Zhang, L., Jasra, A., Zeng, M. Extended H_{\infty} finite time control.


(2014) Zhang, L, Wang, S., Karimi, H. R. & Jasra A. Robust finite-time control of delayed systems.


(2014) Beskos, A., Jasra, A., Kantas N. & Thiery A. On the convergence of adaptive SMC methods. The previous article is here.


(2014) Jasra, A. ABC for a class of time series models.


(2013) Jasra, A., Lee, A., Zhang X. & Yau, C. The alive particle filter.


(2013) Campanella, G., De Iorio, M., Jasra, A. & Chadeau-Hyam, M. The TimeMachine package for inference on stochastic trees. Manual.





This list includes co-authors (past/present, including those on minor publications such as comments), former advisors

and those whom I have been involved with as co-supervisors of students + postdocs.


Christophe Andrieu (University of Bristol, Statistics).

David Balding (University of Melbourne, Maths, Stats & Genetics).

Alex Beskos (UCL, Statistics).

Marc Chadeau (Imperial College London, School of Public Health).

Hock Peng Chan(NUS, Statistics)

Nicolas Chopin (CREST-ENSAE).

Dan Crisan (Imperial College London, Mathematics).

Tom Dean (Cantab Capital)

Pierre Del Moral (UNSW, Maths and Statistics).

Nando De Freitas (University of Oxford, CS).

Maria De Iorio (University College London, Statistics).

Arnaud Doucet (University of Oxford, Statistics).

Kerry Gallagher (Universitie Rennes, Geosciences).

Chris Holmes (University of Oxford, Statistics).

Nikolas Kantas (Imperial College London, Statistics).

Anthony Lee (University of Warwick, Statistics).

Emma McCoy (Imperial College London, Statistics)

Giovanni Montana (Kings College London, Engineering)

David Nott (NUS, Statistics)

Gareth Peters (University College London, Statistics)

Sumeetpal Singh (University of Cambridge, Engineering).

Dave Stephens (McGill University, Statistics).

Alexandre Thiery (NUS, Statistics).

Theo Tsagaris (Tudor Capital).

Nick Whiteley (University of Bristol, Statistics).

Chris Yau (Wellcome Trust, Oxford).

Sinan Yildirim (University of Bristol, Statistics).

Lixian Zhang (Harbin Inst. Technology, Astronautics).


Research Students and Post-Docs






Daniel Paulin (NUS, From July 2014). With Thiery and Nott.


Yan Zhou (NUS, May 2014-present). With Nott.


Victor Ong (NUS, Jan 2014-present). With Nott.


Adam Persing (UCL, Oct 2013-present). With De Iorio, Beskos & Balding.




Pierre Jacob (NUS, Oct 2012-Oct 2013). Now at Oxford.


Research Students:


Please note that I will not be taking new PhD students AY 2014-2015.




Y. Xu (NUS, from Aug 2014). Joint with David Nott.


D. Chen (NUS, 2014-Present). Joint with Alex Thiery.


D. Sen (NUS, 2014-Present). Joint with Alex Thiery (main advisor) and Alex Beskos.


E. A. Muzaffer (NUS, 2013-Present). Joint with Alex Beskos.


S. Wang (CSC, 2013-Present).


C. W. Heng (NUS, 2012-Present). 2nd Supervisor, with Hock Peng Chan.


J. Wang (NUS, 2012-Present). Joint with David Nott.




X. Zhang (IC, 2011-2013), PhD.

Co-Supervisor: Chris Yau.

Now in finance in China.


A.Persing (IC, 2011-2013), PhD.

Co-Supervisor: Dan Crisan and Leonardo Bottolo.

Now post-doc at UCL (London).


E. Ehrlich (IC, 2011-2013), PhD.

Co-Supervisor: Nikolas Kantas.

Now in data analytics in California.


J. Martin (IC, 2008-2012), PhD.

Co-Supervisor: Emma McCoy.

Now post-doc at UCL (London).


A. Cozzini (IC, 2010-2012), PhD.

Co-Supervisor: Giovanni Montana.

Now in finance/sports betting in London.


C. Oduneye (IC, 2010-2011), PhD.

Co-Supervisor: David Stephens.

Now head of credit risk at an investment bank in London/Singapore.


M. Bottone (IC, 2009-2010), MPhil.

Co-Supervisor: David Stephens.

Now doing a PhD.


I. Shiekh (IC, 2008-2010), PhD.

Co-Supervisor: Emma McCoy.

Now an energy analyst in London.








In Semester 1 of AY 2014/15 I will teach ST5219 Bayesian Hierarchical Models. The notes (subject to change) are available here.


The non-assessed problem sheets are here:


Sheet 1


Sheet 2


Sheet 3




In Semester 2 of AY 2013/14 I taught ST5223 Statistical Models: Theory and Applications. The notes are available here.


The non-assessed problem sheets are here:


Linear Models


Generalized Linear Models


Previous Exam Papers:


Exam 2012, with solutions.


Exam 2013, with solutions.




In Semester 1 of AY 2013/14 I taught ST2334 Probability and Statistics. Notes, subject to change.


Some example sheets, in brackets are the associated section of the notes:


Bayes Theorem and the Theorem of Total Probability (1.3)

The Binomial and Poisson Distribution (2.3.1)

The Geometric and Negative Binomial Distribution + MGFs (2.3.3)

Joint Expectations (2.3.4)

Joint PMFs (2.3.4)

Conditional Expectations (2.3.5)

More Conditional Expectations (2.3.5)

Continuous Random Variables (2.4.1)

Notes on Integration (Useful for Sections 2.4 onwards)


Non-Assessed problem sheets:


Sheet 1

Sheet 2

Sheet 3

Sheet 4

Sheet 5

Sheet 6

Sheet 7

Sheet 8

Sheet 9

Sheet 10


Imperial College London


I taught M3S4\M4S4 Applied probability. The notes are available on request.


I taught M3S9\M4S9 Stochastic Simulation. The notes are available on request.


Contact Details


Block S16, Office 04-02

Department of Statistics and Applied Probability

National University of Singapore

6 Science Drive 2,

Singapore, 117546

e-mail: stajaATnus.edu.sg (please replace AT with @)

phone: (+65) 66011410








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