91 |
Stable limit theorems for Markov chains /Kimbleton, Stephen Robert January 1967 (has links)
No description available.
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92 |
Efficient sampling plans in a two-state Markov chain /Bai, Do Sun January 1971 (has links)
No description available.
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93 |
The second gap of the Markoff spectrum of Q(i) /Hansen, Henry Walter January 1973 (has links)
No description available.
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94 |
Contributions to the theory of Markov chains /Winkler, William E. January 1973 (has links)
No description available.
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95 |
Markov chains and potentials.Fraser, Ian Johnson. January 1965 (has links)
No description available.
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96 |
Text classification using a hidden Markov modelYi, Kwan, 1963- January 2005 (has links)
No description available.
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97 |
A functional approach to obtaining weighted Markow-type inequalitiesCarley, Holly K. 01 January 1999 (has links)
No description available.
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98 |
Decomposing Large Markov Chains for Statistical Usage TestingPandya, Chirag 01 January 2000 (has links)
Finite-state, discrete-parameter Markov chains are used to provide a model of the population of software use to support statistical testing of software. Once a Markov chain usage model has been constructed, any number of statistically typical tests can be obtained from the model. Markov mathematics can be applied to obtain values, such as the long run probabilities, that provide information for test planning and analysis of test results. Because Markov chain usage models of industrial-sized systems are often very large, the time and memory required to compute the long run probabilities can be prohibitive. This thesis describes a procedure for automatically decomposing a large Markov chain model 'into several smaller models from which the original model's long run probabilities can be calculated. The procedure supports both parallel processing to reduce the elapsed time, and sequential processing to reduce memory requirements.
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99 |
Convergence of some stochastic matricesWilcox, Chester Clinton. January 1963 (has links)
Call number: LD2668 .T4 1963 W66 / Master of Science
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100 |
Reinforcement learning applied to option pricingMartin, K. S. 01 September 2014 (has links)
A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science. Johannesburg, 2014. / This dissertation considers the pricing of European and American options.
European option prices are determined by the market and can be veri ed by
a closed-form solution to the Black-Scholes model. These options can only be
exercised at the maturity date. American option prices are not derived from the
market and cannot be priced using the same closed-form solution as in the case
of the European options because American options can be exercised at any time
on or before the maturity date. An initial method was investigated in pricing
a European option but could not price American options. Improvements were
made producing two robust option pricing models. The results of which were
compared to the closed-form solution in the case of European options and
a numerical approximation solution in the case of American options. The
improved models showed two signi cant bene ts. The rst bene t is the ability
to price both European and American options and the second is the ability
to calibrate the models to market prices using market data. Changes to the
parameters of the models showed the limitations of each improved model.
In conclusion, the improved methods are e ective procedures for solving the
European and American option pricing problem.
Keywords: European options, American options, Markov Decision Processes,
Kernel-Based Reinforcement Learning, Calibration.
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