Search for dissertations about: "Stochastic processes"
Showing result 16 - 20 of 285 swedish dissertations containing the words Stochastic processes.
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16. Approximation of Infinitely Divisible Random Variables with Application to the Simulation of Stochastic Processes
Abstract : This thesis consists of four papers A, B, C and D. Paper A and B treats the simulation of stochastic differential equations (SDEs). The research presented therein was triggered by the fact that there were not any efficient implementations of the higher order methods for simulating SDEs. READ MORE
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17. Large-scale simulation-based experiments with stochastic models using machine learning-assisted approaches : Applications in systems biology using Markov jump processes
Abstract : Discrete and stochastic models in systems biology, such as biochemical reaction networks, can be modeled as Markov jump processes. The chemical master equation describes how the probability distribution of a biochemical system's states evolves. Unfortunately, solutions to the chemical master equation only exist for trivial problems. READ MORE
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18. Topics in Simulation and Stochastic Analysis
Abstract : Paper A investigates how to simulate a differentiated mean in cases where interchanging differentiation and expectation is not allowed. Three approaches are available, finite differences (FD's), infinitesimal perturbation analysis (IPA) and the likelihood ratio score function (LRSF) method. READ MORE
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19. Probabilistic Sequence Models with Speech and Language Applications
Abstract : Series data, sequences of measured values, are ubiquitous. Whenever observations are made along a path in space or time, a data sequence results. To comprehend nature and shape it to our will, or to make informed decisions based on what we know, we need methods to make sense of such data. READ MORE
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20. Numerical analysis for random processes and fields and related design problems
Abstract : In this thesis, we study numerical analysis for random processes and fields. We investigate the behavior of the approximation accuracy for specific linear methods based on a finite number of observations. Furthermore, we propose techniques for optimizing performance of the methods for particular classes of random functions. READ MORE