List below are research staff, research students,
honours students,
vacation scholars and visitors attached to the Queensland site.
Staff
[Some projects are listed below]

Prof. Phil Pollett,
Chief Investigator and Director (Qld)

Dr Ross McVinish, Research Fellow
PhD students (current)
[Projects are listed below]

Robert Cope,
Animal Movement Between Populations Deduced from Family Trees 
a Test Case on Dugongs in Southern Queensland

Dejan Jovanović,
Fault Detection in Complex and Distributed Systems

Aminath Shausan,
Stochastic Models for Epidemics in Population Networks

Andrew Smith,
Spatially structured metapopulation models within
static and dynamic environments
PhD students (graduated)
[Theses descriptions below]

Fionnuala Buckley,
Analytical Methods for Stochastic Discretetime Metapopulation Models
(PhD awarded April 2011)

Ben
Cairns, Hitting Times for Markov Population Processes
Subject to Catastrophes
(PhD awarded December 2005)
 Ben
Gladwin, Long Time Scale Simulations of Biological Molecular
Systems
(PhD awarded December 2007)
 Dharma Lesmono,
Stochastic Models of Election Timing
(PhD awarded September 2007)

Daniel Pagendam,
Experimental Design and Inference for Population Models
(PhD awarded November 2010)
 Joshua
Ross, Density Dependent Markov Population Processes: Models
and Methodology (PhD awarded March 2007)
 David Sirl, On the Analysis of Absorbing Markov Processes
(PhD awarded May 2008)

Antony Stace,
Volume Weighted Average Price Options
(PhD awarded June 2007)
 Thomas Taimre,
Advances in CrossEntropy Methods
(PhD awarded May 2009)
Other research students (graduated)
[Theses descriptions below]

Nicholas Denman,
Topics in Quasi Stationarity of Markov Chains
(MPhil awarded April 2008)
 Olena
Kravchuk, Trigonometric Scores Rank Procedure with Application
to LongTailed Distributions
(PhD awarded May 2006)
Coursework Masters students

Chung Kai Chan,
Quasistationary Distributions of ContinuousTime
Markov Chains and their Application to
Metapopulation Models, 2009
 Daniel Pagendam, Experimental Design and
Inference for Population Models,
2006
Honours students
 Trent Spears, Extreme Value Theory with Applications, 20112012

Yui Sze (Jessica) Chan,
A Stochastic Metapopulation Model that Accounts for Habitat Dynamics
and Landscapes with Ample Resources, 2011

Alex Ridley,
A NetworkBased Approach to Modelling Epidemics, 2009

Robert Cope,
QuasiBirthandDeath Process, 2008

Jeanette Palmer,
Markovian Models for Stress Release and Transfer, 2006

Leesa Wockner,
Genetic Modelling and Random Walks, 200607
 Zdravko Botev, Stochastic Methods of
Optimization, Simulation and
Learning, 2005
 Andrew Garton, The Fibonacci Sequence and the
Golden
Section in Western Art Music of the Twentieth Century, 2005
 Caitlin James, Measuring Persistence of
Populations using
Importance Sampling
with CrossEntropy, 20032004 [Honours talk]
 Thomas Taimre, Noisy Optimisation via Randomized
Algorithms,
2004
 David Sirl, Uniqueness Conditions for
ContinuousTime Markov Chains,
2003
Advanced Study Program in Science
students
 Robert Cope, Importance Sampling Strategies for
Assigning Hybrid Alleles to Parental Populations, 2006
Vacation scholars
 Ryan Heneghan, Exploring Vaccination Methods for an SIR Epidemic on a Random Population Network with Household Structure and Varying Rates of Infectivity and Severity, 201314
 Patrick Laub, Fixed Point Methods for Loss Networks, 201213
 Trent Spears, Probability with Martingales for Economics and
Finance, 201011
 Trent Spears, The Development and Contemporary Applications
of Stochastic Processes, 200910
 Chung Kai Chan, Quasistationary Distributions in
Markovian Models, 200708
 Robert Cope, Importance Sampling Strategies for
Assigning Hybrid Alleles to Parental Populations, 200607
 Connie McDonagh, Traffic Flow in
Telecommunications Networks,
200506
 Ian Nester, Metapolulation Models,
200506 [report]
 Jeanette Palmer (ICEEM Scholar), Network Models
for Seismicity, 20052006 [report]
 Leesa Wockner, Models for Spatially Structured
Metapopulations,
200506
 Laurel Yu, Modelling Bistability in
Telecommunications Systems,
200506 [report]
 Nathan Jackson, The CE Toolbox,
200405 [report]
 Caitlin James, The CrossEntropy Method for Rare
Event Simulation,
200304
 Thomas Taimre, The CrossEntropy Method for Rare
Event
Simulation and Randomized Optimization, 200304 [reportprograms]
Visiting fellows

Andrew Barbour (University of Zurich and
University of Melbourne), January 2013
 Malwina Luczak (Queen Mary, University of
London), January 2013

Andrew Barbour (University of Zurich and
University of Melbourne), April 2012
 Malwina Luczak (University of Sheffield), November 2011
 Gideon Weiss (University of Haifa),
February 2011

Andrey Lange
(Bauman Moscow State Technical University, Moscow)
November 2009
 Jean Bernard Lasserre (Laboratoire
d'Architecture et d'Analyse des Systèmes,
Centre National de la Recherche Scientifique, Toulouse),
November 2009

Mary Myerscough (University of Sydney), June 2009
 Erik van Doorn (University of Twente), May 2009

Philip O'Neill (University of Nottingham),
March 2009

Sophie Hautphenne (Université Libre de Bruxelles),
February 2009
 Jean Bernard Lasserre (Laboratoire
d'Architecture et d'Analyse des Systèmes,
Centre National de la Recherche Scientifique, Toulouse),
October 2008
 Olena Kravchuk (UQ
School of Land, Crop and Food Sciences),
JulyOctober 2008
 Dharma Lesmono (Parahyangan Catholic University),
September 2007January 2008
 Jean Hu (Northwestern University), FebruaryMay 2007
 Paul Slade (University of Adelaide), MarchApril 2006
 Jiri Tuma (Charles University), OctoberNovember 2005
 Peter Smith (University of Canterbury), July 2005
 Flora Spieksma (University of Leiden), July 2005
 Tony Pakes (University of Western Australia), April 2005
 Anyue Chen (University of Greenwich), April 2005
 Gideon Weiss (University of Haifa), February 2005
 Peter Taylor (MASCOS CI, University of Melbourne), February 2005
 Tony Pakes (University of Western Australia), February 2005
 Nigel Bean (University of Adelaide), February 2005
 Andrew Barbour (University of Zurich), September 2004
 Anyue Chen (University of Greenwich), JulyAugust 2004
 Ruyun Ma (Northwest Normal University, China), February 2004February 2005
 Kin Ping Hui (DSTO, Australia), January 2004
 Soren Asmussen (University of Aarhus), January 2004
 Erik van Doorn (University of Twente), MarchMay 2003
Former staff

Dr Iadine Chadès, Research Fellow
(August 2008August 2009).
Now at CSIRO (Ecosystems Science Division)

Dr Martin O'Hely, Research Fellow
(September 2004November 2007).
Now at the Walter and Eliza Hall Institute of Medical Research
(Bioinformatics Division)

Dr Hanjun Zhang, ARC Centre Fellow
(January 2003December 2007).
Now at Xiangtan University
(School of Mathematics and Computational Science)
Staff and
student
projects
Prof. Phil Pollett, Chief Investigator and Director (Qld)
Phil's research is in the field of mathematical modelling, and is chiefly
concerned with the theory of stochastic processes and applications in
ecology, epidemiology, parasitology, telecommunications and chemical
kinetics.
A current project: General stochastic models for branching
This is joint work with Hanjun Zhang, Anyue Chen (University of Liverpool
and Junping Li (Central South University, Changsha). A common feature of
branching models is that particles or individuals behave independently
producing descendants according to the same rule. However, since particles
may interact, through collision or some other mechanism, this branching
property may be lost. For this reason, more general branching models
have been proposed. We are studying a particularly interesting class,
which we call the weighted (or nonlinear) Markov branching processes. We
are examining questions concerning the existence and uniqueness of such
processes, and criteria for extinction.


Dr Ross McVinish, Research Fellow
Ross has research experience in a
number of areas of probability and statistics, from
Lévy processes and
stochastic processes displaying long memory, to Bayesian nonparametrics,
and computation for Bayesian statistics and time series analysis.
A current project:
Statistical inference for partially observed metapopulations
A common type of metapopulation model is a presenceabsence model in
discrete time with a Markov dependence structure. This type of model only
tracks whether or not each patch within the metapopulation is occupied
and the future state of the metapopulation depends only on the present
state. When all patches within the metapopulation are observed, the
likelihood function can be written explicitly which makes estimation
relatively straightforward. However, it is not always possible to
perform a complete census of a metapopulation. This may be due to costs
associated with a census or due to accessibility or perhaps the locations
of some patches are unknown. In these situations we may attempt to make
inference about the metapopulation based only on the observations from
a small number of patches.
In theory one can still perform likelihood based inference given only
partially observation of the metapopulation. To form the likelihood
function based on partial observation, one takes the likelihood function
based on complete observation and integrates out those unobserved
patches. Typically, the necessary integration is performed using
simulation based methods. However, when the number of patches is large,
the situation where partial observation would be most common, accurate
integration can be very challenging.
An alternative to using an exact likelihood is to use an approximate
likelihood based letting the number of patches increase to infinity. We
provide certain conditions under which the model for the observed
patches converges to a timeinhomogeneous Markov chain. This provides
an explicit approximate likelihood function which can then be used in
either maximum likelihood estimation (MLE) or Bayesian estimation. Under
rather mild conditions, we show that the exact MLE converges to the
proposed approximate MLE as the number of patches increases. Similarly
for the Bayesian estimate, we prove convergence of the exact posterior
distribution to the proposed approximate posterior distribution.
Unfortunately, when the exact likelihood function is replaced by the
asymptotic approximation, a loss of efficiency is incurred. To investigate
the degree of the efficiency loss, we carry out a simulation study using
two metapopulation models. The exact MLE and Bayesian estimates will be
evaluated using a sequential Monte Carlo algorithm.


Dr Iadine Chadès, Research Fellow
(August 2008August 2009)
Iadine has research intersests in mathematical modelling
and decision making in ecology and conservation biology.
She is presently a Research Scientist at CSIRO Ecosystems Science.


Dr Martin O'Hely, Research Fellow
(September 2004November 2007)
Martin is interested in applications of probability to the biological
sciences. The bulk of his research to date has addressed problems in
population genetics.
Martin is presently Research Officer
in the Bioinformatics Division
at the Walter and Eliza Hall Institute (Melbourne).


Dr Hanjun Zhang, ARC Centre Fellow
(January 2003December 2007)
Hanjun is known internationally for contributions to
Markov process theory.
Hanjun presently holds a position in the
School of Mathematics and Computational Science at Xiangtan University.


Fionnuala Buckley, AMSIMASCOS PhD Scholar (200709)
Thesis title:
Analytical Methods for Stochastic Discretetime Metapopulation Models
(PhD awarded April 2011)
The term 'metapopulation' is used to describe a population confined to
a network of geographically separated habitat patches that may suffer
extinction locally and be recolonized through dispersal of individuals from
other patches. Fionnuala has studied discretetime Markov chain models that
account for seasonal variation in metapopulations, assuming that
colonisation and extinction occur in distinct successive phases (one might
envisage an annual cycle, with local populations being susceptible to
extinction during winter, while new populations establish in empty patches
during the spring); a census takes place either at the end of successive
colonisation phases (EC model) or at the end of successive extinction
phases (CE model) and the state of the Markov chain is the state of the
population at a census time. Whilst this approach has become predominant in
the applied metapopulation literature, because it provides a vehicle for
parameter estimation and permits control mechanisms to be investigated
using simple optimisation tools such as dynamic programming, generally only
numerical and simulation methods have been used to analyse discrete time
metapopulation models (and, then, typically only the EC case).
Fionnuala's work provides the first detailed mathematical analysis of these
models. She has derived a law of large numbers, which is used to identify
an approximating (discretetime) deterministic trajectory, and a central
limit theorem, which establishes that the scaled fluctuations about this
trajectory have an approximating autoregressive structure. This was done
for a class of timeinhomogeneous Markov chains that share the salient
features of her metapopulation models, and then applied to those models in
order to draw conclusions about real ecological systems.


Ben Cairns, MASCOS PhD Scholar (200305)
Thesis title: Hitting Times for Markov
Population Processes Subject to Catastrophes
(PhD awarded December 2005)
Ben worked on a range of problems in stochastic modelling of complex
biological systems. He determined the extinction probabilities and
expected extinction times for the Markovian catastrophe process in
continuous time, with a general transition rate function, and gave
necessary and sufficient conditions for explosivity. Ben developed
truncation procedures for estimating persistence in populations which
may be affected by catastrophic events, and which are either unbounded
or have very large ceilings. He developed theory for firstexit time
problems in the context of general piecewisedeterministic processes,
providing a general, robust numerical procedure for estimating
firstexit times and implemented this using techniques from interval
analysis.
Ben is a research scientist in the Cancer Epidemiology Unit
at the University of Oxford having previously held a
postdoctoral position in the School of Biological Sciences
at the University of Bristol.


Yui Sze (Jessica) Chan, PhD student (20122013)
(withdrawn)


Robert Cope, PhD student (20092014)
Thesis title (provisional): Animal Movement Between Populations
Deduced from Family Trees  a Test Case on Dugongs in Southern Queensland
The aim is to develop a new method for estimating animal movements
using information contained in family trees. Movement estimates are
essential to population models that assist natural resource managers to
plan species recovery and to predict the effect of future challenges,
such as humanmediated activities and climate change. I will
evaluate ways of constructing family trees from
genetic data and develop a statistic that describes animal movement
between populations that is based on the families whose members were
sampled in more than one population; empirical data has been sourced
from a longterm markrecapture study of dugongs in Moreton Bay, and
new samples from two adjacent populations.


Nicholas Denman, Parttime MPhil Student
Thesis title: Topics in Quasi Stationarity of Markov Chains
(MPhil awarded July 2007)
Quasistationary distributions are tools that allow one model the
longterm behaviour of processes that "die out". Convergence of standard
truncation methods for evaluating quasistationary distributions is
not always guaranteed, and it is desirable to have algorithms that
avoid truncation. An algorithm which avoids truncation in computing
stationary distributions is the GTH Algorithm. It completely avoids
subtraction, and it was shown that the algorithm computes stationary
distributions with low relative error, and even extremely small stationary
probabilities with high accuracy. A simple principle has been proposed:
that many algorithms in nonnegative arithmetic produce results with
low relative error. Nick explored how this principle applies
to the evaluation of quasistationary distributions. He examined an
algorithm for computing the dominant eigenvectors which uses nonnegative
arithmetic and which gives demonstrably low relative error.
Nicholas is a risk consultant with Energy Edge (Brisbane) Ltd.


Benjamin Gladwin, MASCOS PhD Scholar (200304)
Thesis title: Long Time Scale Simulations of
Biological Molecular Systems
(PhD awarded December 2007)
Ben
works primarily on long timescale molecular
dynamics. Traditionally, molecular processes are seen from a classical
physics perspective and use various forward integration algorithms to
provide thermodynamic information from trajectories. These techniques
are primarily limited by computational resource constraints. A series
of
new algorithms were proposed which achieves low resolution
trajectories of any time scale. One of the difficulties of these
approaches is estimation of the overall time in which a molecular
process takes place. Ben used mean first passage times to provide
an initial trajectory through the molecules' conformation space. This
approach reduces errors introduced by poor timescale estimation. The
practitioner is also provided with a starting point for a trajectory
search
using more traditional deterministic algorithms.
Ben is currently a medical student at Flinders University School of
Medicine.


Dejan Jovanović, AMSIMASCOS PhD Scholar (200911)
Thesis title (provisional): Fault Detection in Complex and Distributed Systems
The primary goal is to develop a theoretical framework based on Markov
processes in order to detect, identify and isolate faults in complex and
distributed systems. The aim is to improve overall safety and reduce any
negative impact on the environment due to a fault. There are three main
tasks. The first is development of local stochastic models, which
need to be capable of interpreting the local environment's state. At the
core is estimation of transition probabilities. The second is
extracting the features of local models in the case of nonfaulty and
faulty operating conditions. In order to assist local models to achieve
satisfactory results, design and implementation of a multiagent system
is proposed. The next task is planning an optimal action to protect the
environment. Finally, the framework has to allow for the possibility of
incorporating local expert knowledge about the system.


Olena Kravchuk, Parttime PhD Student
Thesis title: Trigonometric Scores Rank
Procedures with Applications to
Longtailed Distributions
(PhD awarded May 2006)
Longtailed distributions have become extremely
popular for modelling
stochastic noise in many applications including image analysis,
finance and environmental data analysis. However, often the tail
behaviour of such distributions is not precisely known and
nonparametric
statistical procedures are evoked to perform inference about the
location
and scale characteristics. Olena's work proposes several new rank
procedures that are efficient for a wide range of unimodal, symmetric,
longtailed distributions.
Olena is a Lecturer in Biometrics in the School of Land, Crop and Food
Sciences,
The University of Queensland.


Dharma Lesmono, MASCOS PhD Scholar (200405)
Thesis title: Stochastic Models of Election
Timing
(PhD awarded July 2006)
Dharma made several major contributions to the study of
election forecasting. He derived a model for the early election
call problem that accounts for the possibility of a government
using control tools, termed "boosts", to induce shocks
in the opinion polls by making timely policy announcements or economic
actions. These actions improve the government's popularity and have
some impact on the earlyelection exercise boundary.
He is presently working on some theoretical
extensions the basic framework. He is studying a bounded meanreverting
process, used in the pricing of energy options and in election
forecasting. He aims to provide conditions for existence and uniqueness
of a bounded meanreverting stochastic differential equation whose
drift
coefficient does not satisfy either of the usual Lipschitz or linear
growth conditions.
Dharma is a Lecturer in the Department of Mathematics, Parahyangan
Catholic University, Indonesia.


Daniel Pagendam, AMSIMASCOS PhD Scholar (200709)
Thesis title:
Experimental Design and Inference for Population Models
(PhD awarded November 2010)
Statistical Inference for discretely observed stochastic
models is an active and challenging area of research. However, whilst
much attention has been given to methodologies for parameter estimation,
little work has been done to find optimal schedules for observing these
processes. Daniel addresses the topic of optimal experimental
design for density dependent Markovian models, which routinely encountered in
ecology and epidemiology, with a view to improving the manner in which
data is collected for both controlled and natural experiments.
He frames the search for optimal designs using the classical
Doptimality criterion, so that the series of observations of the
process maximizes the determinant of the Fisher information matrix. The
resulting designs maximize the precision of the maximum likelihood
estimator. In the case of some simple one parameter models, the optimal
observation times can be obtained analytically and he has derive one such
design for the simple death process. He showed that this design is closely
related to the optimal design for the simple birth process.
When models are more complicated, such as when more than one parameter
is involved, the Fisher information matrix, which lies at the heart of
many classical optimality criteria, can rarely be arrived at
analytically. Daniel shows how Gaussian diffusion approximations can be used
to obtain an approximation to the Fisher information matrix for density
dependent Markovian models. When this method is coupled with the
crossentropy method of stochastic optimization, he is able to obtain
optimal observation times rapidly.
Daniel has also shown how diffusion approximations can be used for
parameter estimation in multidimensional, multiparameter epidemic
models with data collected during an outbreak of Russian influenza in a
boarding school serving as an example application of his methodology.
He has examined optimal design for some
commonly used epidemiological models, including the SI, SIS and SIR
epidemics, and has made recommendations for the design of
experimental epidemics or transmission studies.
Dan is currently OCE Postdoctoral Research Fellow within the CSIRO
Mathematics, Informatics and Statistics Division.


Joshua Ross, MASCOS PhD Scholar (200406)
Thesis title: Density Dependent Markov
Population Processes: Models and Methodology
(PhD awarded February 2007)
Joshua developed continuoustime Markov chain
models for
metapopulations that inhabit a dynamic landscape. He established
deterministic and diffusion approximations for these processes, and
derived normal approximations to their (quasi)stationary
distributions.
He also investigated the costs and decisions of controlling
populations that have a negative impact on their habitat. For two
commonly used control regimes, suppression and reduction, he gave
population managers direction on how best to choose the control
parameters. Joshua compared the predicted extinction time estimates
derived from continuoustime Markov chain models with the estimates
from
their appropriate OrnsteinUhlenbeck approximating diffusion and a
simple Brownian motion approximation. In joint work with Thomas Taimre
he developed a method for estimating the parameters of a wide class of
continuoustime Markov chains called densitydependent Markov chains.
The only other known approach to estimating parameters for such
processes
is computationally infeasible when the state space, or uncertainty in
the parameter values, is too large. This new procedure makes use of the
abovementioned diffusion approximations, and in the situations where
the approach is most commonly applied, the estimates improve as the
state space increases in size. Several applications of this procedure
are currently under investigation.
Joshua is currently Lecturer in Applied Mathematics within the
School of Mathematical Sciences, The University of Adelaide,
having previously held a Research Fellowship at King's College
Cambridge UK and a postdoctoral research assistantship
at the University of Warwick.


Aminath Shausan, MASCOS PhD Scholar (201013)
Thesis title (provisional):
Stochastic Models for Epidemics in Population Networks
I will study the spread of infection in "small world" population networks,
where the network is made up of many local links and fewer long range
"shortcuts". When an infection is introduced, it may spread locally and
eventually die out (imagine an infection spreading in a small town). I
will consider the effect of infection via long range links (think of an
infection introduced by an airline passenger) and provide a stochastic
model to describe the spread of the infection throughout the network,
addressing several questions concerning stability and the duration of
the epidemic.
My starting point will be a model where the network is made up of many
local links and fewer long range "shortcuts". Previous researchers
investigated the distribution of the interpoint network distances,
their results being framed in terms of approximations whose accuracy
increases with the size of the network. I propose to study the spread of
epidemics on these networks, using approximation methods (which become
more accurate as the network grows in size) to determine whether or
not there is a critical level, in terms of the limiting proportion of
longrange links, that causes the infection to take hold, and, below
which the epidemic dies out. It is hoped that results of the project
will be of use to epidemiologists and ecologists, and indeed of use
practitioners in fields where "network" is an appropriate paradigm.


David Sirl, AMSIMASCOS PhD Scholar (200506)
Thesis title: On the Analysis of Absorbing
Markov Processes
(PhD awarded May 2008)
David's PhD thesis is concerned with the analysis of absorbing
discretestate Markov processes. He looked at the problem of
establishing the existence of quasistationary distributions (QSDs). He
proved results on exponential convergence rates and established
the existence of a QSD for a particular chemical reaction model.
David studied a wellknown and important exponential convergence
rate: Kingman's decay parameter. He adapted results of MuFa Chen to
give explicit bounds for the decay parameter of a birthdeath process in
terms of the transition rates, an immediate corollary of which is a
necessary and sufficient condition for the decay parameter to be
positive, and analysed these bounds analytically and numerically.
David also investigated an application of the analysis of absorbing
Markov chains in ecology. He considered a threatened species occupying
a habitat which consists of a number of discrete patches. He extended
existing models to allow for the possibility that one can protect certain
patches from disturbances. He discussed deterministic approximations,
which become both computationally necessary, and mathematically more
accurate, as the system size becomes large. He used both the full
stochastic model and the deterministic approximation to investigate the
effect on the viability of the population of two management options:
creating more patches, or protecting existing patches from disturbance
events. The optimal management plan was determined under a given a total
budget and perpatch costs for these two possible actions.
David is currently Lecturer in Statistics, Mathematics Education Centre,
Loughborough University UK, having previously held a research fellowship
in the School of Mathematical Sciences, University of Nottingham UK.


Andrew Smith, AMSIMASCOS PhD Scholar (200912)
Thesis title (provisional): Spatially structured
metapopulation models within static and dynamic environments
A metapopulation is a population that occupies several geographically
separated habitat patches. Although the individual patches may become
empty through local extinction, they may be recolonized through migration
from other patches. There is considerable empirical evidence which
suggests that a balance between migration and extinction is reached
that enables metapopulations to persist for long periods, and there has
been considerable interest in developing methods that account for the
persistence of these populations and which provide an effective means
of studying their longterm behaviour before extinction occurs.
I will begin by looking at basic patchoccupancy models that merely
record which patches are occupied. The main aim is to exploit recent
developments in stochastic network theory by adapting models that were
developed originally for the study of telecommunications systems. By
recording the numbers of individuals in the various patches we can
incorporate local patch dynamics, spatial structure and migration
patterns. I will adopt the powerful diffusion approximation technique
that has been used so effectively in the analysis of patchoccupancy models.


Antony Stace, MASCOS PhD Scholar (200405)
Thesis title:
Volume Weighted Average Price Options
(PhD awarded July 2007)
Antony developed methods for the valuation of a Volume Weighted
Average Price option (VWAP). This is an option which has a strike
which is a VWAP. He obtained a number of results about these options
including an approximation to the price by moment matching and also a
series solution. Antony also investigated a numerical solution to the
partial differential equation that describes the price of the option
by finite differences. This procedure presents a number of challenges;
simple finite difference methods are impractical due to the curse of
dimensionality, so alternating direction implicit and splitting methods
were investigated.
Antony is a risk consultant with Energy Edge (Brisbane) Ltd, having
previously held a position on the financial risk management team of
Pricewaterhouse Coopers (Auckland, New Zealand).


Thomas Taimre, AMSIMASCOS PhD Scholar (200507)
Thesis title: Advances in CrossEntropy Methods
(PhD awarded May 2009)
The CrossEntropy Method is a technique used for solving estimation,
simulation and optimisation problems. Thomas Taimre's work has helped
consolidate our understanding of the CrossEntropy Method and several
of its generalisations, as well as its connection with importance
sampling and other Monte Carlo methods. Thomas has elucidated several new
major applications of crossentropy methodology to optimisation
problems. He has developed new methods within the generalised
crossentropy framework which enable one to construct state and
timedependent importance sampling algorithms, and he has developed a
new algorithm for counting solutions to difficult binaryencoded problems.
Thomas is presently a Lecturer in the UQ School of Mathematics and Physics


