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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

A stochastic realization and model reduction approach to streamflow modeling

Ramos, José A 12 1900 (has links)
No description available.


Harris, Cliff Andrew, 1942- January 1970 (has links)
No description available.

Stochastic models for asset and liability modelling in South Africa or elsewhere

Maitland, Alexander James 16 September 2011 (has links)
Ph. D, Faculty of Science, University of Witwatersrand, 2011 / Research in the area of stochastic models for actuarial use in South Africa is limited to relatively few publications. Until recently, there has been little focus on actuarial stochastic models that describe the empirical stochastic behaviour of South African financial and economic variables. A notable exception is Thomson’s (1996) proposed methodology and model. This thesis presents a collection of five papers that were presented at conferences or submitted for peer review in the South African Actuarial Journal between 1996 and 2006. References to subsequent publications in the field are also provided. Such research has implications for medium and long-term financial simulations, capital adequacy, resilience reserving and asset allocation benchmarks as well as for the immunization of short-term interest rate risk, for investment policy determination and the general quantification and management of risk pertaining to those assets and liabilities. This thesis reviews Thomson’s model and methodology from both a statistical and economic perspective, and identifies various problems and limitations in that approach. New stochastic models for actuarial use in South Africa are proposed that improve the asset and liability modelling process and risk quantification. In particular, a new Multiple Markov-Switching (MMS) model framework is presented for modelling South African assets and liabilities, together with an optimal immunization framework for nominal liability cash flows. The MMS model is a descriptive model with structural features and parameter estimates based on historical data. However, it also incorporates theoretical aspects in its design, thereby providing a balance between purely theoretical models and those based only on empirical considerations.

The behaviour of stochastic rumours.

Belen, Selma January 2008 (has links)
This thesis presents results concerning the limiting behaviour of stochastic rumour processes. The first result involves our published analysis of the evolution for the general initial conditions of the (common) deterministic limiting version of the classical Daley-Kendall and Maki-Thompson stochastic rumour models, [14]. The second result being also part of the general analysis in [14] involves a new approach to stiflers in the rumour process. This approach aims at distinguishing two main types of stiflers. The analytical and stochastic numerical results of two types of stiflers in [14] are presented in this thesis. The third result is that the formulae to find the total number of transitions of a stochastic rumour process with a general case of the Daley-Kendall and Maki-Thompson classical models are developed and presented here, as already presented in [16]. The fourth result is that the problem is taken into account as an optimal control problem and an impulsive control element is introduced to minimize the number of final ignorants in the stochastic rumour process by repeating the process. Our published results are presented in this thesis as appeared in [15] and [86]. Numerical results produced by our algorithm developed for the extended [MT] model and [DK] model are demonstrated by tables in all details of numerical values in the appendices. / Thesis (Ph.D.) - University of Adelaide, School of Mathematical Sciences, 2008

Multi-objective stochastic path planning

Dasgupta, Sumantra 15 May 2009 (has links)
The present research formulates the path planning as an optimization problem with multiple objectives and stochastic edge parameters. The first section introduces different variants of the PP problem and discusses existing solutions to the problem. The next section introduces and solves various versions of the PP model within the scope of this research. The first three versions describe a single entity traveling from a single source to a single destination node. In the first version, the entity has a single objective and abides by multiple constraints. The second version deals with an entity traveling with multiple objectives and multiple constraints. The third version is a modification of the second version where the actual probability distributions of travel times along edges are known. The fourth and final version deals with multiple heterogeneous entities routed from multiple sources (supply nodes) to multiple destinations (demand nodes) along capacitated edges. Each of these formulations is solved by using either exact algorithms or heuristics developed in this research. The performance of each algorithm/heuristic is discussed in the final section. The main contributions of this research are: 1. Provide a framework for analyzing PP in presence of multiple objectives and stochastic edge parameters. 2. Identify candidate constraints where clustering based multi-level programming can be applied to eliminate infeasible edges. 3. Provide an exact O (V.E) algorithm for building redundant shortest paths. 4. Provide an O (V.E+C2) heuristic for generating Pareto optimal shortest paths in presence of multiple objectives where C is the upper bound for path length. The complexity can be further reduced to O (V.E) by using graphical read-out of the Pareto frontier. 5. Provide a cost structure which can capture multiple key probability distribution parameters of edge variables. This is in contrast with usual techniques which just capture single parameters like the mean or the variance of distributions. 6. Provide a MIP formulation to a multi-commodity transportation problem with multiple decision variables, stochastic demands and uncertain edge/route capacities. 7. Provide an alternate formulation to the classic binary facility selection problem.

Stochastic resonance in nanoscale systems

Saha, Aditya Unknown Date
No description available.


Ceekala, Mithun 23 April 2013 (has links)
This thesis presents a new architecture of stochastic Analog-to-Digital converter (ADC). A standard Stochastic ADC uses comparator random offset as the trip point while all the comparators have the same reference voltages. Since the offset of a basic comparator depends on a number of independent random variables, the offset will follow randomly distributed Gaussian function. The input dynamic range of this standard stochastic ADC is ±?. For 90nm technology ? value is around 153mV. A technique is presented that converts overall transfer function of a stochastic ADC i.e. Gaussian distribution into almost uniformly distribution with a wider range. With the proposed technique, an input dynamic range of ± 153mV and ENOB of 4bits of standard stochastic ADC are increased to variable input dynamic range of ±250mV to ±500mV and ENOB of 6bits.

Stochastic Approximation and Its Application in MCMC

Cheng, Yichen 16 December 2013 (has links)
Stochastic approximation has been widely used since first proposed by Herbert Robbins and Sutton Monro in 1951. It is an iterative stochastic method that attempts to find the zeros of functions that cannot be computed directly. In this thesis, we used the technique in several different aspects. It was used in the analysis of large geostatistical data, in the improvement of simulated annealing algorithm also, as well as for NMR protein structure determination. 1. We proposed a resampling based Stochastic approximation method for the analysis of large geostatistical data. The main difficulty that lies in the analysis of geostatistical data is the computation time is extremely long when the sample size becomes large. Our proposed method only use a small portion of the data at each iteration. Each time, we update our estimators based on a randomly selected subset of the data using stochastic approximation. In this way, we use the information from the whole data set while keep the computation time almost irrelevant to the sample size. We proved the consistency of our estimator and showed by simulation study that the computation time is much reduced compared to other existing methods. 2. Simulated Annealing algorithm has been widely used for optimization problems. However, it can not guarantee the global optima to be located unless a logarithmic cooling schedule is used. However, the logarithm rate is so slow that no one can afford such a long cpu time. We proposed a new stochastic optimization algorithm, the so-called simulated stochastic approximation annealing (SAA) algorithm, which is a combination of simulated annealing and the stochastic approximation Monte Carlo (SAMC) algorithm. It is shown that the new algorithm can work with a cooling schedule that decreases much faster than in the logarithmic cooling schedule while guarantee the global optima to be reached when temperature tends to zero. 3. Protein Structure determination is a very important topic in computational biology. It aims to determine different conformations for each protein, which helps to understand biological functions such as protein-protein interactions, protein-DNA interactions and so on. Protein structure determination consists of a series of steps and peak picking is a very important step. It is the prerequisite for all other steps. Manually pick the peaks is very time consuming. To automate this process, several methods have been proposed. However, due to the complexity of NMR spectra, the existing method is hard to distinguish false peaks and true peaks perfectly. The main difficulty lies in identifying true peaks with low intensity and overlapping peaks. We propose to model the spectrum as a mixture of bivariate Gaussian densities and used stochastic approximation Monte Carlo (SAMC) method as the computational approach to solve this problem. Essentially, by putting the peak picking problem into a Bayesian framework, we turned it into a model selection problem. Because Bayesian method will automatically penalize including too much component into the model, our model will distinguish true peaks from noises without pre-process of the data.

Some results on pinching matrices

Ko, Chiu-chan., 高超塵. January 2003 (has links)
published_or_final_version / abstract / toc / Mathematics / Master / Master of Philosophy

Aspects of modelling stochastic volatility

Tsang, Wai-yin, 曾慧賢 January 2000 (has links)
published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy

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