Дисертації з теми "Stochastic simulator"
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Chua, Cheong Wei 1975. "A stochastic pool-based electricity market simulator /." Thesis, McGill University, 2000. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=31045.
Повний текст джерелаIn Part II, a stochastic electricity market simulator (SEMS) is designed using elements of Monte Carlo methods and game theory. Each generator is assumed to operate in a stochastic manner, according to a bid strategy composed of a set of pre-established bid instances and a corresponding set of bid probabilities. The Pool dispatches power and defines prices according to either the LED or OPF models from Part I. Generators can update their bidding strategies according to a profit performance index reflecting their degree of risk tolerance, Chicken (risk averse), Average, and Cowboy (risk taker). SEMS can predict issues such as unintended collusion, as well as to evaluate bidding strategies.
Kim, Daniel D. 1982. "A biological simulator using a stochastic approach for synthetic biology." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33307.
Повний текст джерелаIncludes bibliographical references (leaves 58-59).
Synthetic Biology is a new engineering discipline created by the development of genetic engineering technology. Part of a new engineering discipline is to create new tools to build an integrated engineering environment. In this thesis, I designed and implemented a biological system simulator that will enable synthetic biologists to simulate their systems before they put time into building actual physical cells. Improvements to the current simulators in use include a design that enables extensions in functionality, external input signals, and a GUI that allows user interaction. The significance of the simulation results was tested by comparing them to actual live cellular experiments. The results showed that the new simulator can successfully simulate the trends of a simple synthetic cell.
by Daniel D. Kim.
M.Eng.
Fan, Futing. "Improving GEMFsim: a stochastic simulator for the generalized epidemic modeling framework." Kansas State University, 2016. http://hdl.handle.net/2097/34564.
Повний текст джерелаDepartment of Electrical and Computer Engineering
Caterina M. Scoglio
The generalized epidemic modeling framework simulator (GEMFsim) is a tool designed by Dr. Faryad Sahneh, former PhD student in the NetSE group. GEMFsim simulates stochastic spreading process over complex networks. It was first introduced in Dr. Sahneh’s doctoral dissertation "Spreading processes over multilayer and interconnected networks" and implemented in Matlab. As limited by Matlab language, this implementation typically solves only small networks; the slow simulation speed is unable to generate enough results in reasonable time for large networks. As a generalized tool, this framework must be equipped to handle large networks and contain sufficient support to provide adequate performance. The C language, a low-level language that effectively maps a program to machine in- structions with efficient execution, was selected for this study. Following implementation of GEMFsim in C, I packed it into Python and R libraries, allowing users to enjoy the flexibility of these interpreted languages without sacrificing performance. GEMFsim limitations are not limited to language, however. In the original algorithm (Gillespie’s Direct Method), the performance (simulation speed) is inversely proportional to network size, resulting in unacceptable speed for very large networks. Therefore, this study applied the Next Reaction Method, making the performance irrelevant of network size. As long as the network fits into memory, the speed is proportional to the average node degree of the network, which is not very large for most real-world networks. This study also applied parallel computing in order to advantageously utilize multiple cores for repeated simulations. Although single simulation can not be paralleled as a Markov process, multiple simulations with identical network structures were run simultaneously, sharing one network description in memory.
Boulianne, Laurier. "An algorithm and VLSI architecture for a stochastic particle based biological simulator." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=96690.
Повний текст джерелаGrâce aux récents progrès en informatique et en biologie, il est maintenant possible de simuler et de visualiser des systèmes biologiques de façon virtuelle. Il est attendu que des simulations réalistes produites par ordinateur, in silico, nous permettront d'améliorer notre connaissance des processus biologiques et de favoriser le développement de traitements thérapeutiques efficaces. Les simulateurs biologiques visent à améliorer notre connaissance de processus biologiques qui, autrement, ne pourraient pas être correctement analysés par des études expérimentales. Cette situation requiert le développement de simulateurs de plus en plus précis qui tiennent compte non seulement de la nature stochastique des systèmes biologiques, mais aussi de l'hétérogénéité spatiale ainsi que des effets causés par la grande densité de particules présentes dans ces systèmes. Ce mémoire présente GridCell, un simulateur biologique stochastique original basé sur une représentation microscopique des particules. Ce mémoire présente aussi une architecture parallèle originale accélérant GridCell par presque deux ordres de magnitude. GridCell est un environnement de simulation tridimensionnel qui permet d'étudier le comportement des réseaux biochimique sous différentes influences spatiales, notamment l'encombrement moléculaire ainsi que les effets de recrutement et de localisation des particules. GridCell traque les particules individuellement, ce qui permet d'explorer le comportement de molécules participants en très petits nombres à divers réseaux de signalisation. L'espace de simulation est divisé en une grille 3D discrète qui permet de générer des collisions entre les particules sans avoir à faire de calculs de distance ni de recherches de particules complexes. La compatibilité avec le format SBML permet à des réseaux déjà existants d'être simulés et visualisés. L'interface visuelle permet à l'utilisateur de naviguer de façon intuitive dans la simulation afin d'observer le comportement des espèces à travers le temps et l'espace. Des effets d'encombrement moléculaire sur un système enzymatique de type Michaelis-Menten sont simulés, et les résultats montrent un effet important sur le taux de formation du produit. Tenir compte de millions de particules à la fois est extrêmement demandant pour un ordinateur et, pour pouvoir simuler des cellules complètes avec une résolution spatiale moléculaire en moins d'une journée, un but souvent exprimé en biologie des systèmes, il est essentiel d'accélérer GridCell à l'aide de matériel informatique fonctionnant en parallèle. On propose une architecture sur FPGA combinant le traitement en pipeline, le fonctionnement en mode continu ainsi que l'exécution parallèle. L'architecture peut supporter plusieurs FPGA et l'approche en mode continu permet à l'architecture de supporter très grands systèmes. Une architecture comprenant 25 unités de traitement sur chaque étage du pipeline est synthétisée sur un seul FPGA Virtex-6 XC6VLX760, ce qui permet d'obtenir des gains de performance 76 fois supérieurs à l'implémentation séquentielle de l'algorithme. Ce gain de performance réduit l'écart entre la complexité de la simulation des cellules biologiques et la puissance de calcul des simulateurs avancés. Des travaux futurs sur GridCell pourraient avoir pour objectif de supporter des compartiments de forme très complexe ainsi que des particules haute définition.
Soltani-Moghaddam, Alireza. "Network simulator design with extended object model and generalized stochastic petri-net /." free to MU campus, to others for purchase, 2000. http://wwwlib.umi.com/cr/mo/fullcit?p9999317.
Повний текст джерелаTaleb, B. "The theory and design of a stochastic reliability simulator for large scale systems." Thesis, Open University, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.383689.
Повний текст джерелаPINTO, ROBERTO JOSE. "STOCHASTIC SIMULATOR TO CALCULATE THE AGENTS FINANCIAL FLOW AT BRAZILIAN WHOLESALE ENERGY MARKET." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2002. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=2876@1.
Повний текст джерелаNo novo modelo de livre concorrência do Setor Elétrico Nacional,o Mercado Atacadista de Energia (MAE) foi criado para ser o ambiente onde se processam as compras e vendas de energia de curto prazo. Logo, os agentes que possuem excedentes de energia, provenientes de excesso de geração ou de sobra de contrato, poderão vendê-los no MAE. A situação inversa também pode ocorrer, ou seja, o agente que necessitar de energia para cobrir um deficit de energia ou honrar contratos também poderá comprar energia no MAE. Em cada instante de tempo, os montantes de energia que cada agente poderá comercializar no MAE, assim como o preço de liquidação, não podem ser previstos com exatidão, pois dependem, por exemplo, das condições hidrológicas futuras. Isto acarreta incertezas com relação ao fluxo de caixa futuro dos agentes.No presente trabalho é apresentado um modelo de simulador estocástico capaz de fornecer estimativas futuras do fluxo financeiro de um agente no MAE, considerando-se em detalhe as regras vigentes, analisando- se diversos cenários hidrológicos.
In the new trading model for the Brazilian electricity sector, the Wholesale Energy Market -Mercado Atacadista de Energia - MAE- is the place where all buyers and sellers of electricity can trade and in which the spot price of energy will be determined. In this market the agents can sell the excess of generation or the positive net energy of bilateral contracts. However, lack of generation or negative net energy of bilateral contracts will be exposured to spot market price.The market price and the energy amount that each agent can trade at MAE depends on many factors, such as future hydrological conditions, for example.This fact causes financial flow uncertainties to all market agents. Then, this dissertation shows a model to make the market accounts using the MAE rules and future estimation of generations and consumptions energies. The results of this model could help the agents to forecast the payments and receipts at MAE.
Olsén, Jörgen. "Stochastic Modeling and Simulation of the TCP protocol." Doctoral thesis, Uppsala University, Mathematical Statistics, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3534.
Повний текст джерелаThe success of the current Internet relies to a large extent on a cooperation between the users and the network. The network signals its current state to the users by marking or dropping packets. The users then strive to maximize the sending rate without causing network congestion. To achieve this, the users implement a flow-control algorithm that controls the rate at which data packets are sent into the Internet. More specifically, the Transmission Control Protocol (TCP) is used by the users to adjust the sending rate in response to changing network conditions. TCP uses the observation of packet loss events and estimates of the round trip time (RTT) to adjust its sending rate.
In this thesis we investigate and propose stochastic models for TCP. The models are used to estimate network performance like throughput, link utilization, and packet loss rate. The first part of the thesis introduces the TCP protocol and contains an extensive TCP modeling survey that summarizes the most important TCP modeling work. Reviewed models are categorized as renewal theory models, fixed-point methods, fluid models, processor sharing models or control theoretic models. The merits of respective category is discussed and guidelines for which framework to use for future TCP modeling is given.
The second part of the thesis contains six papers on TCP modeling. Within the renewal theory framework we propose single source TCP-Tahoe and TCP-NewReno models. We investigate the performance of these protocols in both a DropTail and a RED queuing environment. The aspects of TCP performance that are inherently depending on the actual implementation of the flow-control algorithm are singled out from what depends on the queuing environment.
Using the fixed-point framework, we propose models that estimate packet loss rate and link utilization for a network with multiple TCP-Vegas, TCP-SACK and TCP-Reno on/off sources. The TCP-Vegas model is novel and is the first model capable of estimating the network's operating point for TCP-Vegas sources sending on/off traffic. All TCP and network models in the contributed research papers are validated via simulations with the network simulator ns-2.
This thesis serves both as an introduction to TCP and as an extensive orientation about state of the art stochastic TCP models.
Erben, Vojtěch. "Návrh a testování stochastické navigace v TRASI." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-219900.
Повний текст джерелаEchavarria, Gregory Maria Angelica. "Predictive Data-Derived Bayesian Statistic-Transport Model and Simulator of Sunken Oil Mass." Scholarly Repository, 2010. http://scholarlyrepository.miami.edu/oa_dissertations/471.
Повний текст джерелаMontanari, Carlo Emilio. "Development of an event-based simulator for analysing excluded volume effects in a Brownian gas." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14527/.
Повний текст джерелаAzzi, Soumaya. "Surrogate modeling of stochastic simulators." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAT009.
Повний текст джерелаThis thesis is a contribution to the surrogate modeling and the sensitivity analysis on stochastic simulators. Stochastic simulators are a particular type of computational models, they inherently contain some sources of randomness and are generally computationally prohibitive. To overcome this limitation, this manuscript proposes a method to build a surrogate model for stochastic simulators based on Karhunen-Loève expansion. This thesis also aims to perform sensitivity analysis on such computational models. This analysis consists on quantifying the influence of the input variables onto the output of the model. In this thesis, the stochastic simulator is represented by a stochastic process, and the sensitivity analysis is then performed on the differential entropy of this process.The proposed methods are applied to a stochastic simulator assessing the population’s exposure to radio frequency waves in a city. Randomness is an intrinsic characteristic of the stochastic city generator. Meaning that, for a set of city parameters (e.g. street width, building height and anisotropy) does not define a unique city. The context of the electromagnetic dosimetry case study is presented, and a surrogate model is built. The sensitivity analysis is then performed using the proposed method
Ghafari, Jouneghani Farzad. "Photonic quantum information science for stochastic simulation and non-locality." Thesis, Griffith University, 2019. http://hdl.handle.net/10072/386534.
Повний текст джерелаThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Environment and Sc
Science, Environment, Engineering and Technology
Full Text
Basak, Subhasish. "Multipathogen quantitative risk assessment in raw milk soft cheese : monotone integration and Bayesian optimization." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG021.
Повний текст джерелаThis manuscript focuses on Bayesian optimization of a quantitative microbiological risk assessment (QMRA) model, in the context of the European project ArtiSaneFood, supported by the PRIMA program. The primary goal is to establish efficient bio-intervention strategies for cheese producers in France.This work is divided into three broad directions: 1) development and implementation of a multipathogen QMRA model for raw milk soft cheese, 2) studying monotone integration methods for estimating outputs of the QMRA model, and 3) designing a Bayesian optimization algorithm tailored for a stochastic and computationally expensive simulator.In the first part we propose a multipathogen QMRA model, built upon existing studies in the literature (see, e.g., Bonifait et al., 2021, Perrin et al., 2014, Sanaa et al., 2004, Strickland et al., 2023). This model estimates the impact of foodborne illnesses on public health, caused by pathogenic STEC, Salmonella and Listeria monocytogenes, which can potentially be present in raw milk soft cheese. This farm-to-fork model also implements the intervention strategies related to mlik and cheese testing, which allows to estimate the cost of intervention. An implementation of the QMRA model for STEC is provided in R and in the FSKX framework (Basak et al., under review). The second part of this manuscript investigates the potential application of sequential integration methods, leveraging the monotonicity and boundedness properties of the simulator outputs. We conduct a comprehensive literature review on existing integration methods (see, e.g., Kiefer, 1957, Novak, 1992), and delve into the theoretical findings regarding their convergence. Our contribution includes proposing enhancements to these methods and discussion on the challenges associated with their application in the QMRA domain.In the final part of this manuscript, we propose a Bayesian multiobjective optimization algorithm for estimating the Pareto optimal inputs of a stochastic and computationally expensive simulator. The proposed approach is motivated by the principle of Stepwise Uncertainty Reduction (SUR) (see, e.g., Vazquezand Bect, 2009, Vazquez and Martinez, 2006, Villemonteix et al., 2007), with a weighted integrated mean squared error (w-IMSE) based sampling criterion, focused on the estimation of the Pareto front. A numerical benchmark is presented, comparing the proposed algorithm with PALS (Pareto Active Learning for Stochastic simulators) (Barracosa et al., 2021), over a set of bi-objective test problems. We also propose an extension (Basak et al., 2022a) of the PALS algorithm, tailored to the QMRA application case
Hellander, Stefan. "Stochastic Simulation of Reaction-Diffusion Processes." Doctoral thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-198522.
Повний текст джерелаeSSENCE
Drawert, Brian J. "Spatial Stochastic Simulation of Biochemical Systems." Thesis, University of California, Santa Barbara, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3559784.
Повний текст джерелаRecent advances in biology have shown that proteins and genes often interact probabilistically. The resulting effects that arise from these stochastic dynamics differ significantly than traditional deterministic formulations, and have biologically significant ramifications. This has led to the development of computational models of the discrete stochastic biochemical pathways found in living organisms. These include spatial stochastic models, where the physical extent of the domain plays an important role; analogous to traditional partial differential equations.
Simulation of spatial stochastic models is a computationally intensive task. We have developed a new algorithm, the Diffusive Finite State Projection (DFSP) method for the efficient and accurate simulation of stochastic spatially inhomogeneous biochemical systems. DFSP makes use of a novel formulation of Finite State Projection (FSP) to simulate diffusion, while reactions are handled by the Stochastic Simulation Algorithm (SSA). Further, we adapt DFSP to three dimensional, unstructured, tetrahedral meshes in inclusion in the mature and widely usable systems biology modeling software URDME, enabling simulation of the complex geometries found in biological systems. Additionally, we extend DFSP with adaptive error control and a highly efficient parallel implementation for the graphics processing units (GPU).
In an effort to understand biological processes that exhibit stochastic dynamics, we have developed a spatial stochastic model of cellular polarization. Specifically we investigate the ability of yeast cells to sense a spatial gradient of mating pheromone and respond by forming a projection in the direction of the mating partner. Our results demonstrates that higher levels of stochastic noise results in increased robustness, giving support to a cellular model where noise and spatial heterogeneity combine to achieve robust biological function. This also highlights the importance of spatial stochastic modeling to reproduce experimental observations.
Homem, de Mello Tito. "Simulation-based methods for stochastic optimization." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/24846.
Повний текст джерелаMorton-Firth, Carl Jason. "Stochastic simulation of cell signalling pathways." Thesis, University of Cambridge, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.625063.
Повний текст джерелаCheung, Ricky. "Stochastic based football simulation using data." Thesis, Uppsala universitet, Matematiska institutionen, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-359835.
Повний текст джерелаDu, Manuel. "Stochastic simulation studies for honeybee breeding." Doctoral thesis, Humboldt-Universität zu Berlin, 2021. http://dx.doi.org/10.18452/22295.
Повний текст джерелаThe present work describes a stochastic simulation program for modelling honeybee populations under breeding conditions. The program was newly implemented to investigate and optimize different selection strategies. A first study evaluated in how far the program's predictions depend on the underlying genetic model. It was found that the finite locus model rather than the infinitesimal model should be used for long-term investigations. A second study shed light into the importance of controlled mating for honeybee breeding. It was found that breeding schemes with controlled mating are far superior to free-mating alternatives. Ultimately, a final study examined how successful breeding strategies can be designed so that they are sustainable in the long term. For this, short-term genetic progress has to be weighed against the avoidance of inbreeding in the long run. By extensive simulations, optimal selection intensities on the maternal and paternal paths could be determined for different sets of population parameters.
Vasan, Arunchandar. "Timestepped stochastic simulation of 802.11 WLANs." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8533.
Повний текст джерелаThesis research directed by: Dept. of Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
Hashemi, Fatemeh Sadat. "Sampling Controlled Stochastic Recursions: Applications to Simulation Optimization and Stochastic Root Finding." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/76740.
Повний текст джерелаPh. D.
Stocks, Nigel Geoffrey. "Experiments in stochastic nonlinear dynamics." Thesis, Lancaster University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.315224.
Повний текст джерелаAlbertyn, Martin. "Generic simulation modelling of stochastic continuous systems." Thesis, Pretoria : [s.n.], 2004. http://upetd.up.ac.za/thesis/available/etd-05242005-112442.
Повний текст джерелаXu, Zhouyi. "Stochastic Modeling and Simulation of Gene Networks." Scholarly Repository, 2010. http://scholarlyrepository.miami.edu/oa_dissertations/645.
Повний текст джерелаPark, Chuljin. "Discrete optimization via simulation with stochastic constraints." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49088.
Повний текст джерелаChaleeraktrakoon, Chavalit. "Stochastic modelling and simulation of streamflow processes." Thesis, McGill University, 1995. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=28704.
Повний текст джерелаMinoukadeh, Kimiya. "Deterministic and stochastic methods for molecular simulation." Phd thesis, Université Paris-Est, 2010. http://tel.archives-ouvertes.fr/tel-00597694.
Повний текст джерелаWang, Eric Yiqing. "Comparison Between Deterministic and Stochastic Biological Simulation." Thesis, Uppsala universitet, Analys och sannolikhetsteori, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-230732.
Повний текст джерелаMcCloughan, Patrick. "A stochastic simulation model of industrial concentration." Thesis, University of East Anglia, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.333558.
Повний текст джерелаHardy, Mary Rosalyn. "Stochastic simulation in life office solvency assessment." Thesis, Heriot-Watt University, 1994. http://hdl.handle.net/10399/1398.
Повний текст джерелаLiu, Kuo-Ching. "Stochastic simulation-based finite capacity scheduling systems /." The Ohio State University, 1997. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487946776022111.
Повний текст джерелаGeurts, Kevin Richard. "Stochastic simulation of non-Newtonian flow fields /." Thesis, Connect to this title online; UW restricted, 1995. http://hdl.handle.net/1773/9821.
Повний текст джерелаSzekely, Tamas. "Stochastic modelling and simulation in cell biology." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:f9b8dbe6-d96d-414c-ac06-909cff639f8c.
Повний текст джерелаPahle, Jürgen. "Stochastic simulation and analysis of biochemical networks." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2008. http://dx.doi.org/10.18452/15786.
Повний текст джерелаStochastic effects in biochemical networks can affect the functioning of these systems significantly. Signaling pathways, such as calcium signal transduction, are particularly prone to random fluctuations. Thus, an important question is how this influences the information transfer in these pathways. First, a comprehensive overview and systematic classification of stochastic simulation methods is given as methodical basis for the thesis. Here, the focus is on approximate and hybrid approaches. Also, the hybrid solver in the software system Copasi is described whose implementation was part of this PhD work. Then, in most cases, the dynamic behavior of biochemical systems shows a transition from stochastic to deterministic behavior with increasing particle numbers. This transition is studied in calcium signaling as well as other test systems. It turns out that the onset of stochastic effects is very dependent on the sensitivity of the specific system quantified by its divergence. Systems with high divergence show stochastic effects even with high particle numbers and vice versa. Finally, the influence of noise on the performance of signaling pathways is investigated. Simulated and experimentally measured calcium time series are stochastically coupled to an intracellular target enzyme activation process. Then, the information transfer under different cellular conditions is estimated with the information-theoretic quantity transfer entropy. The amount of information that can be transferred increases with rising particle numbers. However, this increase is very dependent on the current dynamical mode of the system, such as spiking, bursting or irregular oscillations. The methods developed in this thesis, such as the use of the divergence as an indicator for the transition from stochastic to deterministic behavior or the stochastic coupling and information-theoretic analysis using transfer entropy, are valuable tools for the analysis of biochemical systems.
Fang, Fang. "A simulation study for Bayesian hierarchical model selection methods." View electronic thesis (PDF), 2009. http://dl.uncw.edu/etd/2009-2/fangf/fangfang.pdf.
Повний текст джерелаLiss, Anders. "Optimizing stochastic simulation of a neuron with parallelization." Thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-324444.
Повний текст джерелаOlsén, Jörgen. "Stochastic modeling and simulation of the TCP protocol /." Uppsala : Matematiska institutionen, Univ. [distributör], 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3534.
Повний текст джерелаPérez, Godofredo. "Stochastic conditional simulation for description of reservoir properties /." Access abstract and link to full text, 1991. http://0-wwwlib.umi.com.library.utulsa.edu/dissertations/fullcit/9203796.
Повний текст джерелаSiu, Daniel. "Stochastic Hybrid Dynamic Systems: Modeling, Estimation and Simulation." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4405.
Повний текст джерелаDu, Manuel [Verfasser]. "Stochastic simulation studies for honeybee breeding / Manuel Du." Berlin : Humboldt-Universität zu Berlin, 2021. http://d-nb.info/1226153194/34.
Повний текст джерелаBrand, Samuel P. C. "Spatial and stochastic epidemics : theory, simulation and control." Thesis, University of Warwick, 2012. http://wrap.warwick.ac.uk/56738/.
Повний текст джерелаL'Ecuyer, Pierre, and Josef Leydold. "rstream: Streams of Random Numbers for Stochastic Simulation." Department of Statistics and Mathematics, Abt. f. Angewandte Statistik u. Datenverarbeitung, WU Vienna University of Economics and Business, 2005. http://epub.wu.ac.at/1544/1/document.pdf.
Повний текст джерелаSeries: Preprint Series / Department of Applied Statistics and Data Processing
Chen, Minghan. "Stochastic Modeling and Simulation of Multiscale Biochemical Systems." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/90898.
Повний текст джерелаDoctor of Philosophy
Modeling and simulation of biochemical networks faces numerous challenges as biochemical networks are discovered with increased complexity and unknown mechanisms. With improvement in experimental techniques, biologists are able to quantify genes and proteins and their dynamics in a single cell, which calls for quantitative stochastic models, or numerical models based on probability distributions, for gene and protein networks at cellular levels that match well with the data and account for randomness. This dissertation studies a stochastic model in space and time of a bacterium’s life cycle— Caulobacter. A two-dimensional model based on a natural pattern mechanism is investigated to illustrate the changes in space and time of a key protein population. However, stochastic simulations are often complicated by the expensive computational cost for large and sophisticated biochemical networks. The hybrid stochastic simulation algorithm is a combination of traditional deterministic models, or analytical models with a single output for a given input, and stochastic models. The hybrid method can significantly improve the efficiency of stochastic simulations for biochemical networks that contain both species populations and reaction rates with widely varying magnitude. The populations of some species may become negative in the simulation under some circumstances. This dissertation investigates negative population estimates from the hybrid method, proposes several remedies, and tests them with several cases including a realistic biological system. As a key factor that affects the quality of biological models, parameter estimation in stochastic models is challenging because the amount of observed data must be large enough to obtain valid results. To optimize system parameters, the quasi-Newton algorithm for stochastic optimization (QNSTOP) was studied and applied to a stochastic (budding) yeast life cycle model by matching different distributions between simulated results and observed data. Furthermore, to reduce model complexity, this dissertation simplifies the fundamental molecular binding mechanism by the stochastic Hill equation model with optimized system parameters. Considering that many parameter vectors generate similar system dynamics and results, this dissertation proposes a general α-β-γ rule to return an acceptable parameter region of the stochastic Hill equation based on QNSTOP. Different optimization strategies are explored targeting different features of the observed data.
Mesogitis, Tassos. "Stochastic simulation of the cure of advanced composites." Thesis, Cranfield University, 2015. http://dspace.lib.cranfield.ac.uk/handle/1826/9216.
Повний текст джерелаGu, Xiaoqing. "The behavior of simulated annealing in stochastic optimization." [Ames, Iowa : Iowa State University], 2008.
Знайти повний текст джерелаChapman, Jacob. "Multi-agent stochastic simulation of occupants in buildings." Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/39868/.
Повний текст джерелаXu, Lina. "Simulation methods for stochastic differential equations in finance." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/134388/1/Lina_Xu_Thesis.pdf.
Повний текст джерелаSato, Hiroyuki. "Stochastic and simulation models of maritime intercept operations capabilities." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Dec%5FSato.pdf.
Повний текст джерелаThesis Advisor(s): Patricia A. Jacobs, Donald P. Gaver. Includes bibliographical references (p.117-119). Also available online.
Posadas, Sergio. "Stochastic simulation of a Commander's decision cycle (SSIM CODE)." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2001. http://handle.dtic.mil/100.2/ADA392113.
Повний текст джерелаThesis advisor(s): Paulo, Eugene P. ; Olson, Allen S. "June 2001." Includes bibliographical references (p. 111-115). Also available in print.