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Search the School of Mathematical SciencesPeople matching "Markov chains"Events matching "Markov chains" 
Alberta Power Prices 15:10 Fri 9 Mar, 2007 :: G08 Mathematics Building University of Adelaide :: Prof. Robert Elliott
Media...The pricing of electricity involves several interesting features. Apart from daily, weekly and seasonal fluctuations, power prices often exhibit large spikes. To some extent this is because electricity cannot be stored. We propose a model for power prices in the Alberta market. This involves a diffusion process modified by a factor related to a Markov chain which describes the number of large generators on line. The model is calibrated and future contracts priced. 

American option pricing in a Markov chain market model 15:10 Fri 19 Mar, 2010 :: School Board Room :: Prof Robert Elliott :: School of Mathematical Sciences, University of Adelaide
This paper considers a model for asset pricing in a world where
the randomness is modeled by a Markov chain rather than Brownian motion.
In this paper we develop a theory of optimal stopping and related
variational inequalities for American options in this model. A version of
Saigal's Lemma is established and numerical algorithms developed.
This is a joint work with John van der Hoek. 

Modelling of Hydrological Persistence in the MurrayDarling Basin for the Management of Weirs 12:10 Mon 4 Apr, 2011 :: 5.57 Ingkarni Wardli :: Aiden Fisher :: University of Adelaide
The lakes and weirs along the lower Murray River in Australia are aggregated and
considered as a sequence of five reservoirs. A seasonal Markov chain model for
the system will be implemented, and a stochastic dynamic program will be used to
find optimal release strategies, in terms of expected monetary value (EMV), for
the competing demands on the water resource given the stochastic nature of
inflows. Matrix analytic methods will be used to analyse the system further, and
in particular enable the full distribution of first passage times between any
groups of states to be calculated. The full distribution of first passage times
can be used to provide a measure of the risk associated with optimum EMV
strategies, such as conditional value at risk (CVaR). The sensitivity of the
model, and risk, to changing rainfall scenarios will be investigated. The effect
of decreasing the level of discretisation of the reservoirs will be explored.
Also, the use of matrix analytic methods facilitates the use of hidden states to
allow for hydrological persistence in the inflows. Evidence for hydrological
persistence of inflows to the lower Murray system, and the effect of making
allowance for this, will be discussed. 

On parameter estimation in population models 15:10 Fri 6 May, 2011 :: 715 Ingkarni Wardli :: Dr Joshua Ross :: The University of Adelaide
Essential to applying a mathematical model to a realworld application is
calibrating the model to data. Methods for calibrating population models
often become computationally infeasible when the populations size (more generally
the size of the state space) becomes large, or other complexities such as
timedependent transition rates, or sampling error, are present. Here we
will discuss the use of diffusion approximations to perform estimation in several
scenarios, with successively reduced assumptions: (i) under the assumption
of stationarity (the process had been evolving for a very long time with
constant parameter values); (ii) transient dynamics (the assumption of stationarity
is invalid, and thus only constant parameter values may be assumed); and, (iii)
timeinhomogeneous chains (the parameters may vary with time) and accounting
for observation error (a sample of the true state is observed). 

Optimal experimental design for stochastic population models 15:00 Wed 1 Jun, 2011 :: 7.15 Ingkarni Wardli :: Dr Dan Pagendam :: CSIRO, Brisbane
Markov population processes are popular models for studying a wide range of
phenomena including the spread of disease, the evolution of chemical reactions
and the movements of organisms in population networks (metapopulations). Our
ability to use these models effectively can be limited by our knowledge about
parameters, such as disease transmission and recovery rates in an epidemic.
Recently, there has been interest in devising optimal experimental designs for
stochastic models, so that practitioners can collect data in a manner that
maximises the precision of maximum likelihood estimates of the parameters for
these models. I will discuss some recent work on optimal design for a variety
of population models, beginning with some simple oneparameter models where the
optimal design can be obtained analytically and moving on to more complicated
multiparameter models in epidemiology that involve latent states and
nonexponentially distributed infectious periods. For these more complex
models, the optimal design must be arrived at using computational methods and we
rely on a Gaussian diffusion approximation to obtain analytical expressions for
Fisher's information matrix, which is at the heart of most optimality criteria
in experimental design. I will outline a simple crossentropy algorithm that
can be used for obtaining optimal designs for these models. We will also
explore the improvements in experimental efficiency when using the optimal
design over some simpler designs, such as the design where observations are
spaced equidistantly in time. 

Inference and optimal design for percolation and general random graph models (Part I) 09:30 Wed 8 Jun, 2011 :: 7.15 Ingkarni Wardli :: Dr Andrei Bejan :: The University of Cambridge
The problem of optimal arrangement of nodes of a random weighted graph
is discussed in this workshop. The nodes of graphs under study are fixed, but
their edges are random and established according to the so called
edgeprobability function. This function is assumed to depend on the weights
attributed to the pairs of graph nodes (or distances between them) and a
statistical parameter. It is the purpose of experimentation to make inference on
the statistical parameter and thus to extract as much information about it as
possible. We also distinguish between two different experimentation scenarios:
progressive and instructive designs.
We adopt a utilitybased Bayesian framework to tackle the optimal design problem
for random graphs of this kind. Simulation based optimisation methods, mainly
Monte Carlo and Markov Chain Monte Carlo, are used to obtain the solution. We
study optimal design problem for the inference based on partial observations of
random graphs by employing data augmentation technique. We prove that the
infinitely growing or diminishing node configurations asymptotically represent
the worst node arrangements. We also obtain the exact solution to the optimal
design problem for proximity (geometric) graphs and numerical solution for
graphs with threshold edgeprobability functions.
We consider inference and optimal design problems for finite clusters from bond
percolation on the integer lattice $\mathbb{Z}^d$ and derive a range of both
numerical and analytical results for these graphs. We introduce innerouter
plots by deleting some of the lattice nodes and show that the ÃÂÃÂ«mostly populatedÃÂÃÂ
designs are not necessarily optimal in the case of incomplete observations under
both progressive and instructive design scenarios. Some of the obtained results
may generalise to other lattices. 

Inference and optimal design for percolation and general random graph models (Part II) 10:50 Wed 8 Jun, 2011 :: 7.15 Ingkarni Wardli :: Dr Andrei Bejan :: The University of Cambridge
The problem of optimal arrangement of nodes of a random weighted graph
is discussed in this workshop. The nodes of graphs under study are fixed, but
their edges are random and established according to the so called
edgeprobability function. This function is assumed to depend on the weights
attributed to the pairs of graph nodes (or distances between them) and a
statistical parameter. It is the purpose of experimentation to make inference on
the statistical parameter and thus to extract as much information about it as
possible. We also distinguish between two different experimentation scenarios:
progressive and instructive designs.
We adopt a utilitybased Bayesian framework to tackle the optimal design problem
for random graphs of this kind. Simulation based optimisation methods, mainly
Monte Carlo and Markov Chain Monte Carlo, are used to obtain the solution. We
study optimal design problem for the inference based on partial observations of
random graphs by employing data augmentation technique. We prove that the
infinitely growing or diminishing node configurations asymptotically represent
the worst node arrangements. We also obtain the exact solution to the optimal
design problem for proximity (geometric) graphs and numerical solution for
graphs with threshold edgeprobability functions.
We consider inference and optimal design problems for finite clusters from bond
percolation on the integer lattice $\mathbb{Z}^d$ and derive a range of both
numerical and analytical results for these graphs. We introduce innerouter
plots by deleting some of the lattice nodes and show that the ÃÂÃÂÃÂÃÂ«mostly populatedÃÂÃÂÃÂÃÂ
designs are not necessarily optimal in the case of incomplete observations under
both progressive and instructive design scenarios. Some of the obtained results
may generalise to other lattices. 

Spectra alignment/matching for the classification of cancer and control patients 12:10 Mon 8 Aug, 2011 :: 5.57 Ingkarni Wardli :: Mr Tyman Stanford :: University of Adelaide
Proteomic timeofflight mass spectrometry produces a spectrum based on the peptides (chains of amino acids) in each patientâs serum sample. The spectra contain data points for an xaxis (peptide weight) and a yaxis (peptide frequency/count/intensity). It is our end goal to differentiate cancer (and subtypes) and control patients using these spectra. Before we can do this, peaks in these data must be found and common peptides to different spectra must be found. The data are noisy because of biotechnological variation and calibration error; data points for different peptide weights may in fact be same peptide. An algorithm needs to be employed to find common peptides between spectra, as performing alignment âby handâ is almost infeasible. We borrow methods suggested in the literature by metabolomic gas chromatographymass spectrometry and extend the methods for our purposes. In this talk I will go over the basic tenets of what we hope to achieve and the process towards this.


Alignment of time course gene expression data sets using Hidden Markov Models 12:10 Mon 5 Sep, 2011 :: 5.57 Ingkarni Wardli :: Mr Sean Robinson :: University of Adelaide
Time course microarray experiments allow for insight into biological processes by measuring gene expression over a time period of interest. This project is concerned with time course data from a microarray experiment conducted on a particular variety of grapevine over the development of the grape berries at a number of different vineyards in South Australia. The aim of the project is to construct a methodology for combining the data from the different vineyards in order to obtain more precise estimates of the underlying behaviour of the genes over the development process. A major issue in doing so is that the rate of development of the grape berries is different at different vineyards.
Hidden Markov models (HMMs) are a well established methodology for modelling time series data in a number of domains and have been previously used for gene expression analysis. Modelling the grapevine data presents a unique modelling issue, namely the alignment of the expression profiles needed to combine the data from different vineyards. In this seminar, I will describe our problem, review HMMs, present an extension to HMMs and show some preliminary results modelling the grapevine data. 

Adventures with group theory: counting and constructing polynomial invariants for applications in quantum entanglement and molecular phylogenetics 15:10 Fri 8 Jun, 2012 :: B.21 Ingkarni Wardli :: Dr Peter Jarvis :: The University of Tasmania
Media...In many modelling problems in mathematics and physics, a standard
challenge is dealing with several repeated instances of a system under
study. If linear transformations are involved, then the machinery of
tensor products steps in, and it is the job of group theory to control how
the relevant symmetries lift from a single system, to having many copies.
At the level of group characters, the construction which does this is
called PLETHYSM.
In this talk all this will be contextualised via two case studies:
entanglement invariants for multipartite quantum systems, and Markov
invariants for tree reconstruction in molecular phylogenetics. By the end
of the talk, listeners will have understood why Alice, Bob and Charlie
love Cayley's hyperdeterminant, and they will know why the three squangles
 polynomial beasts of degree 5 in 256 variables, with a modest 50,000
terms or so  can tell us a lot about quartet trees! 

Asymptotic independence of (simple) twodimensional Markov processes 15:10 Fri 1 Mar, 2013 :: B.18 Ingkarni Wardli :: Prof Guy Latouche :: Universite Libre de Bruxelles
Media...The onedimensional birthand death model is one of the basic processes in applied probability but difficulties appear as one moves to higher dimensions. In the positive recurrent case, the situation is singularly simplified if the stationary distribution has productform. We investigate the conditions under which this property holds, and we show how to use the knowledge to find productform approximations for otherwise unmanageable random walks. This is joint work with Masakiyo Miyazawa and Peter Taylor. 

How fast? Bounding the mixing time of combinatorial Markov chains 15:10 Fri 22 Mar, 2013 :: B.18 Ingkarni Wardli :: Dr Catherine Greenhill :: University of New South Wales
Media...A Markov chain is a stochastic process which is "memoryless",
in that the next state of the chain depends only on the current state,
and not on how it got there. It is a classical result that an ergodic
Markov chain has a unique stationary distribution.
However, classical theory does not provide any information on the rate of
convergence to stationarity. Around 30 years ago, the mixing time of
a Markov chain was introduced to measure the number of steps required
before the distribution of the chain is within some small distance of
the stationary distribution. One reason why this is important is that
researchers in areas such as physics and biology use Markov chains to
sample from large sets of interest. Rigorous bounds on the mixing time
of their chain allows these researchers to have confidence in their results.
Bounding the mixing time of combinatorial Markov chains can be a challenge, and there are only a few approaches available. I will discuss the main methods and give examples for each (with pretty pictures). 

Filtering Theory in Modelling the Electricity Market 12:10 Mon 6 May, 2013 :: B.19 Ingkarni Wardli :: Ahmed Hamada :: University of Adelaide
Media...In mathematical finance, as in many other fields where applied mathematics is a powerful tool, we assume that a model is good enough when it captures different sources of randomness affecting the quantity of interests, which in this case is the electricity prices. The power market is very different from other markets in terms of the randomness sources that can be observed in the prices feature and evolution. We start from suggesting a new model that simulates the electricity prices, this new model is constructed by adding a periodicity term, a jumps terms and a positives mean reverting term. The later term is driven by a nonobservable Markov process. So in order to prices some financial product, we have to use some of the filtering theory to deal with the nonobservable process, these techniques are gaining very much of interest from practitioners and researchers in the field of financial mathematics. 

Markov decision processes and interval Markov chains: what is the connection? 12:10 Mon 3 Jun, 2013 :: B.19 Ingkarni Wardli :: Mingmei Teo :: University of Adelaide
Media...Markov decision processes are a way to model processes which involve some sort of decision making and interval Markov chains are a way to incorporate uncertainty in the transition probability matrix. How are these two concepts related? In this talk, I will give an overview of these concepts and discuss how they relate to each other. 

The Hamiltonian Cycle Problem and Markov Decision Processes 15:10 Fri 2 Aug, 2013 :: B.18 Ingkarni Wardli :: Prof Jerzy Filar :: Flinders University
Media...We consider the famous Hamiltonian cycle problem (HCP) embedded in a Markov decision process (MDP). More specifically, we consider a moving object on a graph G where, at each vertex, a controller may select an arc emanating from that vertex according to a probabilistic decision rule. A stationary policy is simply a control where these decision rules are time invariant. Such a policy induces a Markov chain on the vertices of the graph. Therefore, HCP is equivalent to a search for a stationary policy that induces a 01 probability transition matrix whose nonzero entries trace out a Hamiltonian cycle in the graph. A consequence of this embedding is that we may consider the problem over a number of, alternative, convex  rather than discrete  domains. These include: (a) the space of stationary policies, (b) the more restricted but, very natural, space of doubly stochastic matrices induced by the graph, and (c) the associated spaces of socalled "occupational measures". This approach to the HCP has led to both theoretical and algorithmic approaches to the underlying HCP problem. In this presentation, we outline a selection of results generated by this line of research. 

Modelling and optimisation of group doseresponse challenge experiments 12:10 Mon 28 Oct, 2013 :: B.19 Ingkarni Wardli :: David Price :: University of Adelaide
Media...An important component of scientific research is the 'experiment'. Effective design of these experiments is important and, accordingly, has received significant attention under the heading 'optimal experimental design'. However, until recently, little work has been done on optimal experimental design for experiments where the underlying process can be modelled by a Markov chain. In this talk, I will discuss some of the work that has been done in the field of optimal experimental design for Markov Chains, and some of the work that I have done in applying this theory to doseresponse challenge experiments for the bacteria Campylobacter jejuni in chickens. 

A few flavours of optimal control of Markov chains 11:00 Thu 12 Dec, 2013 :: B18 :: Dr Sam Cohen :: Oxford University
Media...In this talk we will outline a general view of optimal control of a continuoustime Markov chain, and how this naturally leads to the theory of Backward Stochastic Differential Equations. We will see how this class of equations gives a natural setting to study these problems, and how we can calculate numerical solutions in many settings. These will include problems with payoffs with memory, with random terminal times, with ergodic and infinitehorizon value functions, and with finite and infinitely many states. Examples will be drawn from finance, networks and electronic engineering. 

Weak Stochastic Maximum Principle (SMP) and Applications 15:10 Thu 12 Dec, 2013 :: B.21 Ingkarni Wardli :: Dr Harry Zheng :: Imperial College, London
Media...In this talk we discuss a weak necessary and sufficient SMP for Markov modulated optimal control problems. Instead of insisting on the maximum condition of the Hamiltonian, we show that 0 belongs to the sum of Clarke's generalized gradient of the Hamiltonian and Clarke's normal cone of the control constraint set at the optimal control. Under a joint concavity condition on the Hamiltonian the necessary condition becomes sufficient. We give examples to demonstrate the weak SMP and its applications in quadratic loss minimization. 

Ergodicity and loss of capacity: a stochastic horseshoe? 15:10 Fri 9 May, 2014 :: B.21 Ingkarni Wardli :: Professor Ami Radunskaya :: Pomona College, the United States of America
Media...Random fluctuations of an environment are common in ecological and
economical settings. The resulting processes can be described by a
stochastic dynamical system, where a family of maps parametrized by an
independent, identically distributed random variable forms the basis for a
Markov chain on a continuous state space. Random dynamical systems are a
beautiful combination of deterministic and random processes, and they have
received considerable interest since von Neuman and Ulam's seminal work in
the 1940's. Key questions in the study of a stochastic dynamical system
are: does the system have a welldefined average, i.e. is it ergodic?
How does this longterm behavior compare to that of the state
variable in a constant environment with the averaged parameter?
In this talk we answer these questions for a family of maps on the unit
interval that model selflimiting growth. The techniques used can be
extended to study other families of concave maps, and so we conjecture the
existence of a "stochastic horseshoe". 

Stochastic models of evolution: Trees and beyond 15:10 Fri 16 May, 2014 :: B.18 Ingkarni Wardli :: Dr Barbara Holland :: The University of Tasmania
Media...In the first part of the talk I will give a general introduction to phylogenetics, and discuss some of the mathematical and statistical issues that arise in trying to infer evolutionary trees. In particular, I will discuss how we model the evolution of DNA along a phylogenetic tree using a continuous time Markov process.
In the second part of the talk I will discuss how to express the twostate continuoustime Markov model on phylogenetic trees in such a way that allows its extension to more general models. In this framework we can model convergence of species as well as divergence (speciation). I will discuss the identifiability (or otherwise) of the models that arise in some simple cases. Use of a statistical framework means that we can use established techniques such as the AIC or likelihood ratio tests to decide if datasets show evidence of convergent evolution. 

A Random Walk Through Discrete State Markov Chain Theory 12:10 Mon 22 Sep, 2014 :: B.19 Ingkarni Wardli :: James Walker :: University of Adelaide
Media...This talk will go through the basics of Markov chain theory; including how to construct a continuoustime Markov chain (CTMC), how to adapt a Markov chain to include nonmemoryless distributions, how to simulate CTMC's and some key results. 

A Hybrid Markov Model for Disease Dynamics 12:35 Mon 29 Sep, 2014 :: B.19 Ingkarni Wardli :: Nicolas Rebuli :: University of Adelaide
Media...Modelling the spread of infectious diseases is fundamental to protecting ourselves from potentially devastating epidemics. Among other factors, two key indicators for the severity of an epidemic are the size of the epidemic and the time until the last infectious individual is removed. To estimate the distribution of the size and duration of an epidemic (within a realistic population) an epidemiologist will typically use Monte Carlo simulations of an appropriate Markov process. However, the number of states in the simplest Markov epidemic model, the SIR model, is quadratic in the population size and so Monte Carlo simulations are computationally expensive. In this talk I will discuss two methods for approximating the SIR Markov process and I will demonstrate the approximation error by comparing probability distributions and estimates of the distributions of the final size and duration of an SIR epidemic. 

Medical Decision Making 12:10 Mon 11 May, 2015 :: Napier LG29 :: Eka Baker :: University of Adelaide
Media...Practicing physicians make treatment decisions based on clinical trial data every day. This data is based on trials primarily conducted on healthy volunteers, or on those with only the disease in question. In reality, patients do have existing conditions that can affect the benefits and risks associated with receiving these treatments.
In this talk, I will explain how we modified an already existing Markov model to show the progression of treatment of a single condition over time. I will then explain how we adapted this to a different condition, and then created a combined model, which demonstrated how both diseases and treatments progressed on the same patient over their lifetime. 

A SemiMarkovian Modeling of Limit Order Markets 13:00 Fri 11 Dec, 2015 :: Ingkarni Wardli 5.57 :: Anatoliy Swishchuk :: University of Calgary
Media...R. Cont and A. de Larrard (SIAM J. Financial Mathematics, 2013) introduced a tractable stochastic model for the dynamics of a limit order book, computing various quantities of interest such as the probability of a price increase or the diffusion limit of the price process. As suggested by empirical observations, we extend their framework to 1) arbitrary distributions for book events interarrival times (possibly nonexponential) and 2) both the nature of a new book event and its corresponding interarrival time depend on the nature of the previous book event. We do so by resorting to Markov renewal processes to model the dynamics of the bid and ask queues. We keep analytical tractability via explicit expressions for the Laplace transforms of various quantities of interest. Our approach is justified and illustrated by calibrating the model to the five stocks Amazon, Apple, Google, Intel and Microsoft on June 21st 2012. As in Cont and Larrard, the bidask spread remains constant equal to one tick, only the bid and ask queues are modelled (they are independent from each other and get reinitialized after a price change), and all orders have the same size. (This talk is based on our joint paper with Nelson Vadori (Morgan Stanley)). 

Mathematical modelling of the immune response to influenza 15:00 Thu 12 May, 2016 :: Ingkarni Wardli B20 :: Ada Yan :: University of Melbourne
Media...The immune response plays an important role in the resolution of primary influenza infection and prevention of subsequent infection in an individual. However, the relative roles of each component of the immune response in clearing infection, and the effects of interaction between components, are not well quantified.
We have constructed a model of the immune response to influenza based on data from viral interference experiments, where ferrets were exposed to two influenza strains within a short time period. The changes in viral kinetics of the second virus due to the first virus depend on the strains used as well as the interval between exposures, enabling inference of the timing of innate and adaptive immune response components and the role of crossreactivity in resolving infection. Our model provides a mechanistic explanation for the observed variation in viruses' abilities to protect against subsequent infection at short interexposure intervals, either by delaying the second infection or inducing stochastic extinction of the second virus. It also explains the decrease in recovery time for the second infection when the two strains elicit crossreactive cellular adaptive immune responses. To account for intersubject as well as intervirus variation, the model is formulated using a hierarchical framework. We will fit the model to experimental data using Markov Chain Monte Carlo methods; quantification of the model will enable a deeper understanding of the effects of potential new treatments.


SIR epidemics with stages of infection 12:10 Wed 28 Sep, 2016 :: EM218 :: Matthieu Simon :: Universite Libre de Bruxelles
Media...This talk is concerned with a stochastic model for the spread of an epidemic in a closed homogeneously mixing population. The population is subdivided into three classes of individuals: the susceptibles, the infectives and the removed cases. In short, an infective remains infectious during a random period of time. While infected, it can contact all the susceptibles present, independently of the other infectives. At the end of the infectious period, it becomes a removed case and has no further part in the infection process.
We represent an infectious period as a set of different stages that an infective can go through before being removed. The transitions between stages are ruled by either a Markov process or a semiMarkov process. In each stage, an infective makes contaminations at the epochs of a Poisson process with a specific rate.
Our purpose is to derive closed expressions for a transform of different statistics related to the end of the epidemic, such as the final number of susceptibles and the area under the trajectories of all the infectives. The analysis is performed by using simple matrix analytic methods and martingale arguments. Numerical illustrations will be provided at the end of the talk. 

Probabilistic approaches to human cognition: What can the math tell us? 15:10 Fri 26 May, 2017 :: Engineering South S111 :: Dr Amy Perfors :: School of Psychology, University of Adelaide
Why do people avoid vaccinating their children? Why, in groups, does it seem like the most extreme positions are weighted more highly? On the surface, both of these examples look like instances of nonoptimal or irrational human behaviour. This talk presents preliminary evidence suggesting, however, that in both cases this pattern of behaviour is sensible given certain assumptions about the structure of the world and the nature of beliefs. In the case of vaccination, we model people's choices using expected utility theory. This reveals that their ignorance about the nature of diseases like whooping cough makes them underweight the negative utility attached to contracting such a disease. When that ignorance is addressed, their values and utilities shift. In the case of extreme positions, we use simulations of chains of Bayesian learners to demonstrate that whenever information is propagated in groups, the views of the most extreme learners naturally gain more traction. This effect emerges as the result of basic mathematical assumptions rather than human irrationality. 

Stokes' Phenomenon in Translating Bubbles 15:10 Fri 2 Jun, 2017 :: Ingkarni Wardli 5.57 :: Dr Chris Lustri :: Macquarie University
This study of translating air bubbles in a HeleShaw cell containing viscous fluid reveals the critical role played by surface tension in these systems. The standard zerosurfacetension model of HeleShaw flow predicts that a continuum of bubble solutions exists for arbitrary flow translation velocity. The inclusion of small surface tension, however, eliminates this continuum of solutions, instead producing a discrete, countably infinite family of solutions, each with distinct translation speeds. We are interested in determining this discrete family of solutions, and understanding why only these solutions are permitted.
Studying this problem in the asymptotic limit of small surface tension does not seem to give any particular reason why only these solutions should be selected. It is only by using exponential asymptotic methods to study the Stokesâ structure hidden in the problem that we are able to obtain a complete picture of the bubble behaviour, and hence understand the selection mechanism that only permits certain solutions to exist.
In the first half of my talk, I will explain the powerful ideas that underpin exponential asymptotic techniques, such as analytic continuation and optimal truncation. I will show how they are able to capture behaviour known as Stokes' Phenomenon, which is typically invisible to classical asymptotic series methods. In the second half of the talk, I will introduce the problem of a translating air bubble in a HeleShaw cell, and show that the behaviour can be fully understood by examining the Stokes' structure concealed within the problem. Finally, I will briefly showcase other important physical applications of exponential asymptotic methods, including submarine waves and particle chains. 

Stochastic Modelling of Urban Structure 11:10 Mon 20 Nov, 2017 :: Engineering Nth N132 :: Mark Girolami :: Imperial College London, and The Alan Turing Institute
Media...Urban systems are complex in nature and comprise of a large number of individuals that act according to utility, a measure of net benefit pertaining to preferences. The actions of individuals give rise to an emergent behaviour, creating the socalled urban structure that we observe. In this talk, I develop a stochastic model of urban structure to formally account for uncertainty arising from the complex behaviour. We further use this stochastic model to infer the components of a utility function from observed urban structure. This is a more powerful modelling framework in comparison to the ubiquitous discrete choice models that are of limited use for complex systems, in which the overall preferences of individuals are difficult to ascertain. We model urban structure as a realization of a Boltzmann distribution that is the invariant distribution of a related stochastic differential equation (SDE) that describes the dynamics of the urban system. Our specification of Boltzmann distribution assigns higher probability to stable configurations, in the sense that consumer surplus (demand) is balanced with running costs (supply), as characterized by a potential function. We specify a Bayesian hierarchical model to infer the components of a utility function from observed structure. Our model is doublyintractable and poses significant computational challenges that we overcome using recent advances in Markov chain Monte Carlo (MCMC) methods. We demonstrate our methodology with case studies on the London retail system and airports in England. 
News matching "Markov chains" 
Sam Cohen wins prize for best student talk at Aust MS 2009 Congratulations to Mr Sam Cohen, a PhD student within the School, who was awarded the B. H. Neumann Prize for the best student paper at the 2009 meeting of the Australian Mathematical Society for his talk on
Dynamic Risk Measures and Nonlinear Expectations with Markov Chain noise. Posted Tue 6 Oct 09. 
Publications matching "Markov chains"Publications 

On Markovmodulated exponentialaffine bond price formulae Elliott, Robert; Siu, T, Applied Mathematical Finance 16 (1–15) 2009  Discretetime expectation maximization algorithms for Markovmodulated poisson processes Elliott, Robert; Malcolm, William, IEEE Transactions on Automatic Control 53 (247–256) 2008  Pricing Options and Vriance Swaps in MarkovModulated Brownian Markets Elliott, Robert; Swishchuk, A, chapter in Hidden Markov Models in Finance (Vieweg, Springer Science+Business Media) 45–68, 2007  Smoothed Parameter Estimation for a Hidden Markov Model of Credit Quality Korolkiewicz, M; Elliott, Robert, chapter in Hidden Markov Models in Finance (Vieweg, Springer Science+Business Media) 69–90, 2007  The Term Structure of Interest Rates in a Hidden Markov Setting Elliott, Robert; Wilson, C, chapter in Hidden Markov Models in Finance (Vieweg, Springer Science+Business Media) 15–30, 2007  A Markov analysis of social learning and adaptation Wheeler, Scott; Bean, Nigel; Gaffney, Janice; Taylor, Peter, Journal of Evolutionary Economics 16 (299–319) 2006  A hidden Markov approach to the forward premium puzzle Elliott, Robert; Han, B, International Journal of Theoretical and Applied Finance 9 (1009–1020) 2006  Datarecursive smoother formulae for partially observed discretetime Markov chains Elliott, Robert; Malcolm, William, Stochastic Analysis and Applications 24 (579–597) 2006  Option pricing for GARCH models with Markov switching Elliott, Robert; Siu, T; Chan, L, International Journal of Theoretical and Applied Finance 9 (825–841) 2006  Option Pricing for Pure Jump Processes with Markov Switching Compensators Elliott, Robert, Finance and Stochastics 10 (250–275) 2006  New Gaussian mixture state estimation schemes for discrete time hybrid GaussMarkov systems Elliott, Robert; Dufour, F; Malcolm, William, The 2005 American Control Conference, Portland, OR, USA 08/06/05  Simulating catchmentscale monthly rainfall with classes of hidden Markov models Whiting, Julian; Thyer, M; Lambert, Martin; Metcalfe, Andrew, The 29th Hydrology and Water Resources Symposium, Rydges Lakeside, Canberra, Australia 20/02/05  General smoothing formulas for Markovmodulated Poisson observations Elliott, Robert; Malcolm, William, IEEE Transactions on Automatic Control 50 (1123–1134) 2005  Hidden Markov chain filtering for a jump diffusion model Wu, P; Elliott, Robert, Stochastic Analysis and Applications 23 (153–163) 2005  Hidden Markov filter estimation of the occurrence time of an event in a financial market Elliott, Robert; Tsoi, A, Stochastic Analysis and Applications 23 (1165–1177) 2005  Ramaswami's duality and probabilistic algorithms for determining the rate matrix for a structured GI/M/1 Markov chain Hunt, Emma, The ANZIAM Journal 46 (485–493) 2005  Risksensitive filtering and smoothing for continuoustime Markov processes Malcolm, William; Elliott, Robert; James, M, IEEE Transactions on Information Theory 51 (1731–1738) 2005  State and mode estimation for discretetime jump Markov systems Elliott, Robert; Dufour, F; Malcolm, William, Siam Journal on Control and Optimization 44 (1081–1104) 2005  A probabilistic algorithm for finding the rate matrix of a blockGI/M/1 Markov chain Hunt, Emma, The ANZIAM Journal 45 (457–475) 2004  Development of NonHomogeneous and Hierarchical Hidden Markov Models for Modelling Monthly Rainfall and Streamflow Time Series Whiting, Julian; Lambert, Martin; Metcalfe, Andrew; Kuczera, George, World Water and Environmental Resources Congress (2004), Salt Lake City, Utah, USA 27/06/04  Robust Mary detection filters and smoothers for continuoustime jump Markov systems Elliott, Robert; Malcolm, William, IEEE Transactions on Automatic Control 49 (1046–1055) 2004  Arborescences, matrixtrees and the accumulated sojourn time in a Markov process Pearce, Charles; Falzon, L, chapter in Stochastic analysis and applications Volume 3 (Nova Science Publishers) 147–168, 2003  A Probabilistic algorithm for determining the fundamental matrix of a block M/G/1 Markov chain Hunt, Emma, Mathematical and Computer Modelling 38 (1203–1209) 2003  A complete yield curve description of a Markov interest rate model Elliott, Robert; Mamon, R, International Journal of Theoretical and Applied Finance 6 (317–326) 2003  A nonparametric hidden Markov model for climate state identification Lambert, Martin; Whiting, Julian; Metcalfe, Andrew, Hydrology and Earth System Sciences 7 (652–667) 2003  Robust parameter estimation for asset price models with Markov modulated volatilities Elliott, Robert; Malcolm, William; Tsoi, A, Journal of Economic Dynamics & Control 27 (1391–1409) 2003  Portfolio optimization, hidden Markov models, and technical analysis of P&Fcharts Elliott, Robert; Hinz, J, International Journal of Theoretical and Applied Finance 5 (385–399) 2002  Supporting maintenance strategies using Markov models AlHassan, K; Swailes, D; Chan, J; Metcalfe, Andrew, IMA Journal of Management Mathematics 13 (17–27) 2002  Hidden Markov chain filtering for generalised Bessel processes Elliott, Robert; Platen, E, chapter in Stochastics in Finite and Infinite Dimensions  in honor of Gopinath Kallianpur (Birkhauser) 123–143, 2001  Robust Mary detection filters for continuoustime jump Markov systems Elliott, Robert; Malcolm, William, The 40th IEEE Conference on Decision and Control (CDC), Orlando, Florida 04/12/01  On the existence of a quasistationary measure for a Markov chain Lasserre, J; Pearce, Charles, Annals of Probability 29 (437–446) 2001  Hidden state Markov chain time series models for arid zone hydrology Cigizoglu, K; Adamson, Peter; Lambert, Martin; Metcalfe, Andrew, International Symposium on Water Resources and Environmental Impact Assessment (2001), Istanbul, Turkey 11/07/01  Entropy, Markov information sources and Parrondo games Pearce, Charles, UPoN'99: Second International Conference, Adelaide, Australia 12/07/99  Levelphase independence for GI/M/1type markov chains Latouche, Guy; Taylor, Peter, Journal of Applied Probability 37 (984–998) 2000 
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