Masters of Commerce / If share price fluctuations follow a simple random walk then it implies that forecasting models based on historical growth rates have little ability to forecast acceptable share price movements over a certain period. The simple random walk description of share price dynamics is obtained when a large number of investors have equal probability to buy or sell based on their own opinion. This simple random walk description of the stock market is in essence the Efficient Market Hypothesis, EMT. EMT is the central concept around which financial modelling is based which includes the Black-Scholes model and other important theoretical underpinnings of capital market theory like mean-variance portfolio selection, arbitrage pricing theory (APT), security market line and capital asset pricing model (CAPM). These theories, which postulates that risk can be reduced to zero sets the foundation for option pricing and is a key component in financial software packages used for pricing and forecasting in the financial industry. The model used by Black and Scholes and other models mentioned above are Gaussian, i.e. they exhibit a random nature. This Gaussian property and the existence of expected returns and continuous time paths (also Gaussian properties) allow the use of stochastic calculus to solve complex Black- Scholes models. However, if the markets are not Gaussian then the idea that risk can be. (educed to zero can lead to a misleading and potentially disastrous sense of security on the financial markets. This study project test the null hypothesis - share prices on the JSE follow a random walk - by means of graphical techniques such as symmetry plots and Quantile-Quantile plots to analyse the test distributions. In both graphical techniques evidence for the rejection of normality was found. Evidenceleading to the rejection of the hypothesis was also found through nonparametric or distribution free methods at a 1% level of significance for Anderson-Darling and Runs test.

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Inom linja ̈r algebra har varje vektorrum ett s ̊a kallat dualrum, vilket är ett vektorrum bestående av alla linjära funktioner från det ursprungliga rummet till sin kropp. Att beräkna dimensionen av ett dualrum tillhörande ett ändlig-dimensionellt vektorrum är relativt enkelt, för oändlig-dimensionella vektorrum är det mer komplicerat. Den sats vi ska diskutera, Erdős–Kaplansky Satsen, ämnar lösa den frågan med påståendet att ett dualrum tillhörande ett oändlig-dimensionellt vektorrum har dimension lika med sin kardinalitet.

Society relies on increasingly complex software and hardware systems, hence techniques capable of proving that they behave as expected are of great and growing interest. Formal verification procedures employ mathematically sound reasoning to address this need. This thesis proposes novel techniques for the verification and falsification of expressive specifications on timed and infinite-state systems. An expressive specification language allows the description of the intended behaviour of a system via compact formal statements written at an abstraction level that eases the review process. Falsifying a specification corresponds to identifying an execution of the system that violates the property (i.e. a witness). The capability of identifying witnesses is a key feature in the iterative refinement of the design of a system, since it provides a description of how a certain error can occur. The designer can analyse the witness and take correcting actions by refining either the description of the system or its specification. The contribution of this thesis is twofold. First, we propose a semantics for Metric Temporal Logic that considers four different models of time (discrete, dense, super-discrete and super-dense). We reduce its verification problem to finding an infinite fair execution (witness) for an infinite-state system with discrete time. Second, we define a novel SMT-based algorithm to identify such witnesses. The algorithm employs a general representation of such executions that is both informative to the designer and provides sufficient structure to automate the search of a witness. We apply the proposed techniques to benchmarks taken from software, infinite-state, timed and hybrid systems. The experimental results highlight that the proposed approaches compete and often outperform specific (application tailored) techniques currently used in the state of the art.

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This thesis is concerned with automata over infinite trees. They are given a labeled infinite tree and accept or reject this tree based on its labels. A generalization of these automata with binary decisions are weighted automata. They do not just decide 'yes' or 'no', but rather compute an arbitrary value from a given algebraic structure, e.g., a semiring or a lattice. When passing from unweighted to weighted formalisms, many problems can be translated accordingly. The purpose of this work is to determine the feasibility of solving the inclusion problem for automata on infinite trees and its generalization to weighted automata, the infimum aggregation problem.

I den här uppsatsen presenteras ett nytt sätt att procedurellt generera pseudo-oändligadungeons som kan traverseras i tre dimensioner. För att hålla genereringen koherent bådeunder och mellan spelsessioner utgår genereringen från ett ”seed”. Ett seed garanterar sammainnehåll på samma plats varje gång miljön genereras. Med en metod som är baserad iL-system genererar vi här en maze dungeon med tvådimensionell grammatikexpansion ochvi undersöker sedan hur duglig den är. Vi försöker få svaret på hur avancerade L-system somkrävs för att nå en nivå av variation som känns tillräcklig för en miljö av pseudo-oändligkaraktär. Variationstestet samlar data från ett urval områden i den färdiggenererade dungeonen.Resultaten visar på att den utvecklade metoden i det här projektet fungerar väl förändamålet. / In this paper we present a novel way of procedurally generating pseudo infinite dungeonsthat can be traversed in three dimensions. To ensure consistent generation both duringand between playing sessions the generation is ”seeded”. Seeded generation guarantees thesame content in the same place every time the environment is generated. Using a methodbased in L-systems we generate a maze dungeon with two dimensional grammar expansionand explore its viability. We try to find the needed complexity of the L-system to achievea level of variation that feels adequate for an environment of pseudo infinite character.The variation tests collect data from sample areas in the generated dungeons. The resultsindicate that the method used in this project works well for the intended purpose.

In this dissertation, we focus on two research topics related to mixture models. The first topic is Adaptive Rejection Metropolis Simulated Annealing for Detecting Global Maximum Regions, and the second topic is Bayesian Model Selection for Nonlinear Mixed Effects Model. In the first topic, we consider a finite mixture model, which is used to fit the data from heterogeneous populations for many applications. An Expectation Maximization (EM) algorithm and Markov Chain Monte Carlo (MCMC) are two popular methods to estimate parameters in a finite mixture model. However, both of the methods may converge to local maximum regions rather than the global maximum when multiple local maxima exist. In this dissertation, we propose a new approach, Adaptive Rejection Metropolis Simulated Annealing (ARMS annealing), to improve the EM algorithm and MCMC methods. Combining simulated annealing (SA) and adaptive rejection metropolis sampling (ARMS), ARMS annealing generate a set of proper starting points which help to reach all possible modes. ARMS uses a piecewise linear envelope function for a proposal distribution. Under the SA framework, we start with a set of proposal distributions, which are constructed by ARMS, and this method finds a set of proper starting points, which help to detect separate modes. We refer to this approach as ARMS annealing. By combining together ARMS annealing with the EM algorithm and with the Bayesian approach, respectively, we have proposed two approaches: an EM ARMS annealing algorithm and a Bayesian ARMS annealing approach. EM ARMS annealing implement the EM algorithm by using a set of starting points proposed by ARMS annealing. ARMS annealing also helps MCMC approaches determine starting points. Both approaches capture the global maximum region and estimate the parameters accurately. An illustrative example uses a survey data on the number of charitable donations. The second topic is related to the nonlinear mixed effects model (NLME). Typically a parametric NLME model requires strong assumptions which make the model less flexible and often are not satisfied in real applications. To allow the NLME model to have more flexible assumptions, we present three semiparametric Bayesian NLME models, constructed with Dirichlet process (DP) priors. Dirichlet process models often refer to an infinite mixture model. We propose a unified approach, the penalized posterior Bayes factor, for the purpose of model comparison. Using simulation studies, we compare the performance of two of the three semiparametric hierarchical Bayesian approaches with that of the parametric Bayesian approach. Simulation results suggest that our penalized posterior Bayes factor is a robust method for comparing hierarchical parametric and semiparametric models. An application to gastric emptying studies is used to demonstrate the advantage of our estimation and evaluation approaches. / Ph. D.