Ajay Jasra


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


Degrees, Positions, Editorial Work & Events


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 at NUS. I am an associate

member of the stochastic analysis group at Imperial College London.


Short-term academic visitor at:


University of Bristol (Statistics) and

University College London (Statistics) (May-July, 2012).

Institute of Statistical Mathematics (Jul-Sept, 2007).


I am currently associate editor at:


American Journal of Algorithms and Computing (2012-)

Statistics and Computing (2011-)

Stat (2012-).


The 2013-2014 NUS statistics seminar schedule is here. I am seminar chair, please contact me if you want to speak at DSAP.


In Feb 2014 the fourth Singapore conference on statistical science will be held at NUS. I am co-organizer (chair A/P Lim).


In April 2014 there is a one month programme on Monte Carlo methods in Cambridge. I am co-organizer (chair Dr S. Singh).




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 (35)


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


(2014) Cozzini, A., Jasra, A. Montana G. & Persing A. Bayesian Mixture of Lasso Regressions., Comput. Stat. Data Anal. (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) Jasra, A., Kantas, N. & Ehrlich E. Approximate inference for observation driven time series. TOMACS (to appear). arXiv.


(2014) Beskos, A., Crisan, D. & Jasra, A. On the stability of SMC in high-dimensions. Ann. Appl. Probab (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) 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 Feynman-Kac 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 (9)


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


(2014) Beskos, A., Jasra, A., Kantas N. & Thiery A. On the convergence of adaptive SMC methods. A substantial revision

of the previous article here.


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


(2013) Jasra, A. On the behaviour of the backward Feynman-Kac formula.


(2013) Yildirim, S., Singh S. S., Dean T. & Jasra A. Parameter estimation in HMMs with intractable likelihoods via SMC.


(2013) Persing A. & Jasra A. Twisting the alive particle filter.


(2013) Kantas, N, Beskos, A. & Jasra, A. SMC for inverse problems: a case study for Navier Stokes.


(2013) Jasra A. & Wang J. An adaptive SMC method for permanents.


(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 (UCL, Genetics Institute).

Alex Beskos (NUS, 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 (DPFM)

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).


Research Students and Post-Docs




Yan Zhou (NUS, from Apr 2014). With Nott.


Victor Ong (NUS, from Jan 2014). 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:




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). Joint with Alex Thiery.


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 London.


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 finance in New York City.


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.








From January 2014 I will teach ST5223 Statistical Models: Theory and Applications. The notes are available here.


Assessment 1 is here. This is to be handed in by 18th February by 5pm to me. Solutions.


Assessment 2 is here, with data. This is to be handed in by 18th March by 5pm to me. A model report is here.


For the tutorial of 04/03 the PDF of question (2) and solution.


The non-assessed problem sheets are here:


Linear Models


Generalized Linear Models




Linear Models


Generalized Linear Models


Previous Exam Papers:


Exam 2012, with solutions.


Exam 2013, with solutions.




From August-November 2013 I taught ST2334 Probability and Statistics. Notes, subject to change.

It should be possible (at least for those at NUS) to view a video of the lectures online:


Lecture 1

Lecture 2

Lecture 3

Lecture 4

Lecture 5

Lecture 6

Lecture 7

Lecture 8

Lecture 9

Lecture 10

Lecture 11

Lecture 12

Lecture 13

Lecture 14

Lecture 15

Lecture 16

Lecture 17

Lecture 18

Lecture 19

Lecture 20


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




Sheet 1

Sheet 2

Sheet 3

Sheet 4

Sheet 5

Sheet 6

Sheet 7

Sheet 8

Sheet 9

Sheet 10



Assessment 1. To be handed in by 5pm on Thursday 19th September on DSAP level 7, pigeon holes.


Assessment 1 Solutions.


Assessment 2. To be handed in by 5pm on Thursday 7th November on DSAP level 7, pigeon holes.


Assessment 2 Solutions.


Examination: Here is a check-list that should assist your revision.


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 04-04

Department of Statistics and Applied Probability

National University of Singapore

6 Science Drive 2,

Singapore, 117546

e-mail: staja@nus.edu.sg

phone: (+65) 66011410








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