__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,

10/2005-02/2006:
Research Associate, Statistics,

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).

__Funding __

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.

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.

(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.

(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 inference、Stat. 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,

ESIAM Proc.

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

ESIAM Proc.

(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.

__Collaborators__

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 (

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 (

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 (

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__

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:

**Current:**

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). 2^{nd}
Supervisor, with Hock Peng Chan.

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

**Completed:**

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.

__Teaching__

__NUS__

__ST5223__

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 18^{th} February by 5pm to me. Solutions.

Assessment 2 is here, with data.
This is to be handed in by 18^{th} 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:

Solutions:

Previous Exam Papers:

__ST2334__

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 6

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:

Solutions:

Assessment:

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

Assessment 1 Solutions.

Assessment 2. To be handed in
by 5pm on Thursday 7^{th} 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