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Stochastic Galerkin Model Updating of Randomly Distributed ParametersNizamiev, Kamil 10 May 2011 (has links)
No description available.
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A Study of Stochastic Resonance in a Climate modelOlsson, Agnes, Jernmark Burrows, Ebba January 2022 (has links)
Historically, the earth’s fluctuation between interglacial and glacial climates has been observedto have a period of 105 years [1]. However, simulations of the global average temperature didn’tmanage to reproduce this cycle period until 1982, when Benzi et al. [2] introduced the combinationof long-term variations in incoming solar radiation and stochastic noise in an energy balancemodel. Using an energy balance model means that the change in global average temperature isset as proportional to the difference in ingoing and outgoing energy. The result of the simulationsdemonstrated so-called stochastic resonance, where small stochastic perturbations amplified thepattern of the variation in insolation, causing a pattern of large changes in the global averagetemperature, i.e. changes in the climate. The stochastic perturbations model unpredictable shorttime scale phenomena like the weather. Our study aimed to reproduce the result of Benzi et al.[2] and to investigate the model and its parameters. The presence of a 105-year climatic cycle insimulated data was found. The combination of both noise and varying incoming solar radiationwas necessary to observe the 105-year cycle. The characteristics of the climate cycle pattern did,however, vary greatly depending on the values of constants in the model, illustrating how themodel and constants were imprecise. Therefore, no conclusions can be drawn from this studyabout the earth’s current or future climate. However, the study still confirms that stochasticnoise is an important part of modeling the climate, and manages to simulate the earth’s observed105-year climate cycle.
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Volta River Flows Stochastic Modelling and ForecastingAddo, C.K.O. 12 1900 (has links)
<p> The Volta River Authority (VRA) is responsible for the generation
and transmission of power in Ghana. For this purpose, VRA owns
and operates two hydroelectric generating stations (at Akosombo
and Kpong) with a combined installed capacity of 1060 Kw. The
Akosombo plant is served by the Lake Volta Reservoir. Prediction
of inflows into the Volta Lake is one of the important functions
of the reservoir management group.</p> <p>For this project, some of the more recent methods of mathematical modelling are investigated with a view to building a simple stochastic model which adequately represents and forecasts the Volta river average monthly flow. The Box-Jenkins family of
models are employed in this exercise. A parsimonious model in the
form of a seasonal autoregressive integrated moving average
(SARIMA) model is arrived at which adequately models and
forecasts the available data.</p> <p>The selected model is reasonably easy to set up, has few parameters to estimate and therefore making the updating of these parameters a relatively simple task.</p> / Thesis / Master of Engineering (MEngr)
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Diagonal Representation of the Doubly Stochastic LimitDatta, Biswanath 04 1900 (has links)
The main result of this thesis is the following theorem. If A is a non-negative symmetric matrix, then there exists a diagonal matrix D such that D A D is doubly stochastic, if and only if A has total support. The relevant theory is discussed and some other results of similar nature are also obtained, including a sufficient and necessary condition for the uniqueness of D above. / Thesis / Master of Science (MSc)
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Essays on Empirical Dynamic Stochastic General Equilibrium ModelsHou, Keqiang 09 1900 (has links)
<p> The overall goal of this thesis is to provide a deeper understanding of the role of dynamic stochastic general equilibrium (DSGE) models as foundations upon which empirical work is conducted. This is a very broad topic with a large existing literature. For this purpose, my dissertation focuses on applying the tools and rich structure of DSGE models to answer questions that have hitherto been studied only by using a reduced-form characterization. I have chosen to look at two specific macroeconomic questions of interest: the economic consequences of oil price shocks in Canada and the role of intangible capital (IC) in explaining cyclical dynamics of S&P500 earnings. Chapter 2 look at the economic consequences of oil price shocks in a structural vector autoregressions (VAR) framework. Chapter 3 builds on this by developing an open economy DSGE model to investigate the impact of oil price shocks on the aggregate Canadian economy and to quantify the relative contribution of U.S. and Canadian monetary policy in transmitting oil price shocks. Chapter 4 studies another interesting macroeconomic phenomenon: the excess volatility of aggregate profits. We embed intangible capital into an otherwise standard real business cycle (RBC) model to examine the role of intangible capital in driving cyclical dynamics of S&P500 earnings. A common feature of my papers is the application of Bayesian time series techniques to macroeconomic data to pursue new insights on "the impact of oil price shocks on economic activities", "the role of monetary policy in transmitting oil price shocks" in new open economic macroeconomics (NOEM) literature and "intangible capital and corporate earnings" in U.S. business cycle literature.</p> / Thesis / Doctor of Philosophy (PhD)
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Optimal Inference with a Multidimensional Multiscale StatisticDatta, Pratyay January 2023 (has links)
We observe a stochastic process 𝑌 on [0,1]^𝑑 (𝑑 ≥ 1) satisfying 𝑑𝑌(𝑡)=𝑛¹/²𝑓(𝑡)𝑑𝑡 + 𝑑𝑊(𝑡), 𝑡 ∈ [0,1]^𝑑, where 𝑛 ≥ 1 is a given scale parameter (`sample size'), 𝑊 is the standard Brownian sheet on [0,1]^𝑑 and 𝑓 ∈ L₁([0,1]^𝑑) is the unknown function of interest. We propose a multivariate multiscale statistic in this setting and prove that the statistic attains a subexponential tail bound; this extends the work of 'Dumbgen and Spokoiny (2001)' who proposed the analogous statistic for 𝑑=1.
In the process, we generalize Theorem 6.1 of 'Dumbgen and Spokoiny (2001)' about stochastic processes with sub-Gaussian increments on a pseudometric space, which is of independent interest. We use the proposed multiscale statistic to construct optimal tests (in an asymptotic minimax sense) for testing 𝑓 = 0 versus (i) appropriate Hölder classes of functions, and (ii) alternatives of the form 𝑓 = 𝜇_𝑛𝕀_{𝐵_𝑛}$, where 𝐵_𝑛 is an axis-aligned hyperrectangle in [0,1]^𝑑 and 𝜇_𝑛 ∈ ℝ; 𝜇_𝑛 and 𝐵_𝑛 unknown. In Chapter 3 we use this proposed multiscale statistics to construct honest confidence bands for multivariate shape-restricted regression including monotone and convex functions.
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Signal in the Noise? The Effect of Non-Invasive Brain Stimulation on Contrast PerceptionParrott, Danielle Elizabeth 13 July 2020 (has links)
A longstanding question in studies of cortical stimulation has been how does stimulation affect brain functioning and cognition, and what are its mechanisms of action. Brain stimulation has been traditionally seen either as a disrupting intervention or as a procedure to enhance cortical excitability and promote improvement in various modality from motor to visual performance. In vision, several hypotheses have been proposed and many experimental paradigms have been used to study how transcranial magnetic stimulation (TMS) and direct current stimulation, particularly transcranial random noise stimulation (tRNS) affect visual discrimination. Psychophysical paradigms are particularly useful to measure visual performance, whereby a stimulus is progressively changed from easy to difficult to perceive it, and accuracy threshold can be measured by titrating the stimulus discriminability. Stimuli that vary in contrast are typically used to study low-level visual functions and it is well known that neurons within the early visual areas in the brain, and primarily V1, are tuned to stimuli involved in contrast discrimination. Here we used an orientation discrimination task to study changes in contrast detection by varying stimulus contrast across different levels (Experiment 1, Chapter 2). We used neuro-navigated single-pulse TMS at different intensities to determine whether behavioral
response changed linearly as a function of stimulus discriminability independently of TMSintensity, or whether TMS affected behavior depending on TMS intensity and contrast level. Moreover, we tested whether TMS had an effect selective for the field contralateral to stimulation or whether effects could be seen across the entire visual field. Single pulse TMS was delivered to left V1 while participants performed a 2-alternative forced choice orientation discrimination (OD) of one of two Gabor patches presented on either side of fixation at 5 contrast levels and 4 TMS intensities. Participants' performance on OD increased at all contrast levels in the right visual field (contralateral to stimulation) at 80% of phosphene thresholds (PT, individually measured at baseline). Furthermore, when TMS was delivered at 60% of PT, we found improved performance in the right visual field that
was selective for the medium contrast, while performance increased at the highest contrast irrespective of TMS intensity, in the field ipsilateral to stimulation, thus both visual fields were affected by TMS, albeit differently. Since the improvement effects might be explained as the result of added noise to the system that paradoxically improves performance for justbelow threshold stimuli (middle contrasts), in Experiments 1 and 2 (Chapter 3) we used transcranial random noise stimulation, a neuromodulation procedure known to enhance cortical excitability when delivered at high frequencies, to further test the hypothesis that
brain stimulation might work through a mechanism of stochastic resonance, whereby adding noise to a nonlinear system, the brain in our case, might paradoxically promote better performance by enhancing stimulus discriminability. This might happen only for selective stimulus intensities and stimulation strength. Based on previous successful work, we tested contrast discrimination changes as a function of four different tRNS low intensity levels of stimulation, and we found a decrease in performance selective for the condition with subthreshold stimuli and at .750 mA stimulation intensity. This result might indicate
that low intensity stimulation is not enough to promote enhancement of stimuli under the stochastic mechanism effect, thereby suggesting that higher ranges of stimulation are necessary to create the optimal conditions for improvement.
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Using Modeling And Simulation To Evaluate Disease Control MeasuresAtkins, Tracy 01 January 2010 (has links)
This dissertation introduced several issues concerning the analysis of diseases by showing how modeling and simulation could be used to assist in creating health policy by estimating the effects of such policies. The first question posed was how would education, vaccination and a combination of these two programs effect the possible outbreak of meningitis on a college campus. After creating a model representative of the transmission dynamics of meningitis and establishing parameter values characteristic of the University of Central Florida main campus, the results of a deterministic model were presented in several forms. The result of this model was the combination of education and vaccination would eliminate the possibility of an epidemic on our campus. Next, we used simulation to evaluate how quarantine and treatment would affect an outbreak of influenza on the same population. A mathematical model was created specific to influenza on the UCF campus. Numerical results from this model were then presented in tabular and graphical form. The results comparing the simulations for quarantine and treatment show the best course of action would be to enact a quarantine policy on the campus thus reducing the maximum number of infected while increasing the time to reach this peak. Finally, we addressed the issue of performing the analysis stochastically versus deterministically. Additional models were created with the progression of the disease occurring by chance. Statistical analysis was done on the mean of 100 stochastic simulation runs comparing that value to the one deterministic outcome. The results for this analysis were inconclusive, as the results for meningitis were comparable while those for influenza appeared to be different.
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On the Modeling of TCP Latency and ThroughputZheng, Dong 03 August 2002 (has links)
In this thesis, a new model for the slow start phase based on the discrete evolutions of congestion window is developed, and we integrate this part into the improved TCP steady state model for a better prediction performance. Combining these short and steady state models, we propose an extensive stochastic model which can accurately predict the throughput and latency of the TCP connections as functions of loss rate, round-trip time (RTT), and file size. We validate our results through simulation experiments. The results show that our model?s predictions match the simulation results better than the Padhye and Cardwell's stochastic models, about 75% improvement in the accuracy of performance predictions for the steady state and 20% improvement for the short-lived TCP flows.
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Stochastic modeling of fatigue crack growthVerma, Dhirendra January 1990 (has links)
No description available.
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