This work is a study of the relationship between Brownian motion and elementary, linear partial differential equations. In the text, I have shown that Brownian motion is a Markov process, and that Brownian motion itself, and certain Stochastic processes involving Brownian motion are also martingales. In particular, Dynkin's formula for Brownian motion was shown. Using Dynkin's formula and Brownian motion, I then constructed solutions for the classical Dirichlet problem and the heat equation, given by Δu=0 and ut= 1/2Δu+g, respectively. I have shown that the bounded solution is unique if Brownian motion will always exit the domain of the function once it has started at a point in the domain. The heat equation also has a unique bounded solution.

Two new spatio-temporal hidden Markov models (HMM) are introduced in this thesis, with the purpose of capturing the persistent, spatially non-homogeneous nature of climate influence on annual rainfall series observed in Australia. The models extend the two-state HMM applied by Thyer (2001) by relaxing the assumption that all sites are under the same climate control. The Switch HMM (SHMM) allows at-site anomalous states, whilst still maintaining a regional control. The Regional HMM (RHMM), on the other hand, allows sites to be partitioned into different Markovian state regions. The analyses were conducted using a Bayesian framework to explicitly account for parameter uncertainty and select between competing hypotheses. Bayesian model averaging was used for comparison of the HMM and its generalisations. The HMM, SHMM and RHMM were applied to four groupings of four sites located on the Eastern coast of Australia, an area that has previously shown evidence of interannual persistence. In the majority of case studies, the RHMM variants showed greatest posterior weight, indicating that the data favoured the multiple region RHMM over the single region HMM or the SHMM variants. In no cases does the HMM produce the maximum marginal likelihood when compared to the SHMM and RHMM. The HMM state series and preferred model variants were sensitive to the parameterisation of the small-scale site-to-site correlation structure. Several parameterisations of the small-scale Gaussian correlation were trialled, namely Fitted Correlation, Exponential Decay Correlation, Empirical and Zero Correlation. Significantly, it was shown that annual rainfall data outliers can have a large effect on inference for a model that uses Gaussian distributions. The practical value of this modelling is demonstrated by the conditioning of the event based point rainfall model DRIP on the hidden state series of the HMM variants. Short timescale models typically underestimate annual variability because there is no explicit structure to incorporate long-term persistence. The two-state conditioned DRIP model was shown to reproduce the annual variability observed to a greater degree than the single state DRIP. / PhD Doctorate

One of the implicit assumptions made in research related to inventory control is to keep products indefinitely in inventory to meet future demand. However, such an assumption is not true for a large wide of products characterized by a limited lifetime. The economic impact of managing such products led to substantial work in perishable inventory control literature. Investigations developed so far underline the complexity of modeling perishable inventory. Moreover, the dependency of the lifetime to temperature conditions in which products are handled adds more complexity since the lifetime of products stemming from the same order may vary from product to another. In this context, the ability of Time Temperature Integrators to capture the effects of temperature variations on products' lifetime, offers an opportunity to reduce spoilage and therefore ensure product's freshness and safety. The general aim of this thesis is to model perishable inventory systems. Particularly, three different problem areas are considered. The first one concerns perishable inventory with fixed lifetime, often referred as Fixed Life Perishability Problem, where an approximate (r;Q) inventory policy is developed. This model relaxes some assumptions made in previous related works. The second problem considered is a (T; S) perishable inventory system with random lifetime. Results of this model contribute to the development of a theoretical background for perishable inventory systems which are based on Markov renewal process approach. The third area incorporates the impact of temperature variations on products' lifetime throughout inventory systems that use TTIs technology. More general settings regarding the demand and the lifetime distributions are considered throughout simulation analysis. The economic relevance stemming from the deployment of this technology is therefore quantified.

Motor proteins are microscopic biological machines that convert chemical energy into mechanical motion and work. They power a diverse range of biological processes, for example the swimming and crawling motion of bacteria, intracellular transport, and muscle contraction. Understanding the physical basis of these processes is interesting in its own right, but also has an interesting potential for applications in medicine and nanotechnology. The ongoing rapid developments in single molecule experimental techniques make it possible to probe these systems on the single molecule level, with increasing temporal and spatial resolution. The work presented in this thesis is concerned with physical modeling of motor proteins on the molecular scale, and with theoretical challenges in the interpretation of single molecule experiments. First, we have investigated how a small groups of elastically coupled motors collaborate, or fail to do so, when producing strong forces. Using a simple model inspired by the motor protein PilT, we find that the motors counteract each other if the density becomes higher than a certain threshold, which depends on the asymmetry of the system. Second, we have contributed to the interpretation of experiments in which the stepwise motion of a motor protein is followed in real time. Such data is naturally interpreted in terms of first passage processes. Our main conclusions are (1) Contrary to some earlier suggestions, the stepping events do not correspond to the cycle completion events associated with the work of Hill and co-workers. We have given a correct formulation. (2) Simple kinetic models predict a generic mechanism that gives rise to correlations in step directions and waiting times. Analysis of stepping data from a chimaeric flagellar motor was consistent with this prediction. (3) In the special case of a reversible motor, the chemical driving force can be extracted from statistical analysis of stepping trajectories. / QC 20100820

In our daily life, we often experience waiting in a queue to receive some kind of service. Some customers do not join the queue at the end like other normal customers, and try to cut in the queue hoping to have a shorter waiting time and a higher level of satisfaction. This behaviour is called customer interjection. Some of these customers only try to cut in queue, while some others try to find excuses for interjection. For instance, the first-come-first-served (FCFS) service discipline is usually assumed in public places like restaurants, banks, airports, and supermarkets. However, customer interjections can still be seen in these places. In telecommunications networks, to test the efficiency of transmission, artificial packages are inserted into the normal traffic in a random manner. These interjections can affect the waiting time of other customers in queue. Such interjections may reduce the waiting time of interjecting customers, but increase the waiting time and dissatisfaction of others. In this work, an M/M/1 queueing system with customer interjection is investigated. The arrival of customers to the system is assumed to be a Poisson process with arrival rate . The service times for customers are independent and identically distributed random variables with an exponential distribution with rate . Customers are dispersed into normal customers and interjecting customers. A normal customer joins the queue at the end, and an interjecting customer tries to cut in the queue and occupy a position as close to the head of the queue as possible. Two parameters are introduced to describe the interjection behaviour: the percentage of customers interjecting and the tolerance level of interjection by individual customers who are already waiting in the queue. Using matrix-analytic methods and stochastic comparison methods, the waiting times of normal customers and interjecting customers are being studied. The impacts of the two parameters on the waiting times are analyzed in detail, and the implications of the results are discussed with numerical examples. It is found that the waiting times are sensitive to the tolerance level of interjection by individual customers. It is also found that eliminating customer interjection would be always beneficial to normal customers and arbitrary customers though it would not always be so for interjecting customers.

The focus of this thesis is on the application of decision models to the economic evaluation of health care technologies. The primary objective addresses the correct choice of modelling technique, as the attributes of the chosen technique could have a significant impact on the process, as well as the results, of an evaluation. Separate decision models, a Markov process and a discrete event simulation (DES) model are applied to a case study evaluation comparing alternative adjuvant therapies for early breast cancer. The case study models are built and analysed as stochastic models: whereby probability distributions are specified to represent the uncertainty about the true values of the model input parameters. Three secondary objectives are also specified. Firstly, the empirical application of the alternative decision models requires the specification of a 'modelling process' that is not well defined in the health economics literature. Secondly, a comparison of alternative methods for specifying probability distributions to describe the uncertainty in the model's input parameters is undertaken. The final secondary objective covers the application of methods for valuing the collection of additional information to inform the resource allocation decision. The empirical application of the two relevant modelling techniques clarifies the potential advantages derived from the increased flexibility provided by DES over Markov models. The thesis concludes that the use of DES should be strongly considered if either of the following issues appear relevant: model parameters are a function of the time spent in particular states, or the data describing the timing of events are not in the form of transition probabilities. The full description of the modelling process provides a resource for health economists wanting to use decision models. No definitive process is established, however, as there exist competing methods for various stages of the modelling process. The main conclusion from the comparison of methods for specifying probability distributions around the input parameters is that the theoretically specified distributions are most likely to provide a common baseline for comparisons between evaluations. The central question that remains to be addressed is which method is the most theoretically correct? The application of a Vol analysis provides useful insights into the methods employed and leads to the identification of particular methodological issues requiring future research in this area.

The main focus of this thesis is Markovian decision processes with an emphasis on incorporating time-dependence into the system dynamics. When considering such decision processes, we provide value equations that apply to a large range of classes of Markovian decision processes, including Markov decision processes (MDPs) and semi-Markov decision processes (SMDPs), time-homogeneous or otherwise. We then formulate a simple decision process with exponential state transitions and solve this decision process using two separate techniques. The first technique solves the value equations directly, and the second utilizes an existing continuous-time MDP solution technique. To incorporate time-dependence into the transition dynamics of the process, we examine a particular decision process with state transitions determined by the Erlang distribution. Although this process is originally classed as a generalized semi-Markov decision process, we re-define it as a time-inhomogeneous SMDP. We show that even for a simply stated process with desirable state-space properties, the complexity of the value equations becomes so substantial that useful analytic expressions for the optimal solutions for all states of the process are unattainable. We develop a new technique, utilizing phase-type (PH) distributions, in an effort to address these complexity issues. By using PH representations, we construct a new state-space for the process, referred to as the phase-space, incorporating the phases of the state transition probability distributions. In performing this step, we effectively model the original process as a continuous-time MDP. The information available in this system is, however, richer than that of the original system. In the interest of maintaining the physical characteristics of the original system, we define a new valuation technique for the phase-space that shields some of this information from the decision maker. Using the process of phase-space construction and our valuation technique, we define an original system of value equations for this phasespace that are equivalent to those for the general Markovian decision processes mentioned earlier. An example of our own phase-space technique is given for the aforementioned Erlang decision process and we identify certain characteristics of the optimal solution such that, when applicable, the implementation of our phase-space technique is greatly simplified. These newly defined value equations for the phase-space are potentially as complex to solve as those defined for the original model. Restricting our focus to systems with acyclic state-spaces though, we describe a top-down approach to solution of the phase-space value equations for more general processes than those considered thus far. Again, we identify characteristics of the optimal solution to look for when implementing this technique and provide simplifications of the value equations where these characteristics are present. We note, however, that it is almost impossible to determine a priori the class of processes for which the simplifications outlined in our phase-space technique will be applicable. Nevertheless, we do no worse in terms of complexity by utilizing our phase-space technique, and leave open the opportunity to simplify the solution process if an appropriate situation arises. The phase-space technique can handle time-dependence in the state transition probabilities, but is insufficient for any process with time-dependent reward structures or discounting. To address such decision processes, we define an approximation technique for the solution of the class of infinite horizon decision processes whose state transitions and reward structures are described with reference to a single global clock. This technique discretizes time into exponentially distributed length intervals and incorporates this absolute time information into the state-space. For processes where the state-transitions are not exponentially distributed, we use the hazard rates of the transition probability distributions evaluated at the discrete time points to model the transition dynamics of the system. We provide a suitable reward structure approximation using our discrete time points and guidelines for sensible truncation, using an MDP approximation to the tail behaviour of the original infinite horizon process. The result is a finite-state time-homogeneous MDP approximation to the original process and this MDP may be solved using standard existing solution techniques. The approximate solution to the original process can then be inferred from the solution to our MDP approximation. / Thesis (Ph.D.) -- University of Adelaide, School of Mathematical Sciences, 2008

A useful measure of quality of a global optimisation algorithm such as simulated annealing is the length of time it must be run to reach a global optimum within a certain accuracy. Such a performance measure assists in choosing and tuning algorithms. This thesis proposes an approach to obtaining such a measure through successive approximation of a generic stochastic global optimisation algorithm with a sequence of stochastic processes culminating in backtracking adaptive search. The overall approach is to approximate the progress of an optimisation algorithm with that of a model process, backtracking adaptive search. The known convergence rate of the model then provides an estimator of the unknown convergence rate of the original algorithm. Parameters specifying this model are chosen based on observation of the optimisation algorithm. The optimisation algorithm may first be approximated with a time-inhomogeneous Markovian process defined on the problem range. The distribution of the number of iterations to convergence for this averaged range process is shown to be identical with that of the original process. This process is itself approximated by a time-homogeneous Markov process in the range, the asymptotic averaged range process. This approximation is defined for all Markovian optimisation algorithms and a weak condition under which its convergence time closely matches that of the original algorithm is developed. The asymptotic averaged range process is of the same form as backtracking adaptive search, the final stage of approximation. Backtracking adaptive search is an optimisation algorithm which generalises pure adaptive search and hesitant adaptive search. In this thesis the distribution of the number of iterations for which the algorithm runs in order to reach a sufficiently extreme objective function level is derived. Several examples of backtracking adaptive search on finite problems are also presented, including special cases that have received attention in the literature. Computational results of the entire approximation framework are reported for several examples. The method can be applied to any optimisation algorithm to obtain an estimate of the time required to obtain solutions of a certain quality. Directions for further work in order to improve the accuracy of such estimates are also indicated.

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L’une des hypothèses implicites faites dans la recherche liée à la gestion des stocks est de maintenir les produits indéfiniment pour satisfaire la demande future. Toutefois, cette hypothèse n’est pas vraie pour les produits caractérisés par une durée de vie limitée. L’impact économique de la gestion de tels produits a conduit à d’importants travaux de recherche. Les investigations développées jusqu’ici ont souligné la complexité de modéliser les stocks de produits périssables. En plus, la dépendance de la durée de la vie à la température à laquelle les produits sont maintenus crée un challenge majeur en termes de modélisation puisque la durée de vie des produits provenant d’une même commande peut varier d’un produit à un autre. La capacité des nouvelles technologies de contrôle de fraîcheur telles que les intégrateurs temps - température de capturer les effets des variations de la température sur la durée de vie offre une opportunité de réduire les pertes et donc d’assurer la fraîcheur des produits vendus. L’objectif général de cette thèse est de modéliser des politiques de gestion de stock des produits périssables. En premier lieu, nous nous intéressons `a la politique (r;Q) o`u les produits ont une durée de vie constante. Le modèle que nous proposons relaxe certaines hypothèses formulées dans les précédents travaux. La deuxième politique considérée est la politique (T; S) où les produits ont une durée de vie aléatoire. Enfin, nous étudions l’impact des nouvelles technologies de contrôle de fraîcheur des produits périssables sur la gestion des stocks. Nous nous intéressons à la pertinence économique découlant du déploiement des intégrateurs temps températures dans la gestion des stocks. / One of the implicit assumptions made in research related to inventory control is to keep products indefinitely in inventory to meet future demand. However, such an assumption is not true for a large wide of products characterized by a limited lifetime. The economic impact of managing such products led to substantial work in perishable inventory control literature. Investigations developed so far underline the complexity of modeling perishable inventory. Moreover, the dependency of the lifetime to temperature conditions in which products are handled adds more complexity since the lifetime of products stemming from the same order may vary from product to another. In this context, the ability of Time Temperature Integrators to capture the effects of temperature variations on products’ lifetime, offers an opportunity to reduce spoilage and therefore ensure product’s freshness and safety. The general aim of this thesis is to model perishable inventory systems. Particularly, three different problem areas are considered. The first one concerns perishable inventory with fixed lifetime, often referred as Fixed Life Perishability Problem, where an approximate (r;Q) inventory policy is developed. This model relaxes some assumptions made in previous related works. The second problem considered is a (T; S) perishable inventory system with random lifetime. Results of this model contribute to the development of a theoretical background for perishable inventory systems which are based on Markov renewal process approach. The third area incorporates the impact of temperature variations on products’ lifetime throughout inventory systems that use TTIs technology. More general settings regarding the demand and the lifetime distributions are considered throughout simulation analysis. The economic relevance stemming from the deployment of this technology is therefore quantified.