Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 35 for “"Bayesian Computation"”.
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Transport maps for accelerated Bayesian computation
Bayesian inference provides a probabilistic framework for combining prior knowledge with mathematical models and observational data. Characterizing a Bayesian posterior probability distribution can be a computationally challenging undertaking, however, particularly when evaluations of the posterior …
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Approximate Bayesian Computation for Complex Dynamic Systems
… by real applications in biology, I propose computational strategies for Bayesian inference in contexts where standard Monte Carlo methods cannot be directly applied due to the high complexity of the dynamic model and/or data limitations.</p><p> Chapter 2 focuses on stochastic bionetwork …
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Accelerating Bayesian Computation in Earth Remote Sensing Problems
… parameters. This thesis presents a more robust Bayesian approach to quantify the uncertainty of the retrieval, but this is computationally intractable given the high dimensionality of the problem. In many Bayesian inverse problems, however, there exists a low-dimensional likelihood-informed …
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Summary statistics and sequential methods for approximate Bayesian computation
… impossible to calculate likelihoods. Approximate Bayesian computation (ABC) is a method of inference for such models. It replaces calculation of the likelihood by a step which involves simulating artificial data for different parameter values, and comparing summary statistics of the simulated data …
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General and Efficient Bayesian Computation through Hamiltonian Monte Carlo Extensions
… is a state-of-the-art sampling algorithm for Bayesian computation. Popular probabilistic programming languages Stan and PyMC rely on HMC’s generality and efficiency to provide automatic Bayesian inference platforms for practitioners. Despite its wide-spread use and numerous success stories, …
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Large-scale Bayesian computation using Stochastic Gradient Markov Chain Monte Carlo
… one of the most popular methods for inference on Bayesian models, scales poorly with dataset size. This is because it requires one or more calculations over the full dataset at each iteration. Stochastic gradient Markov chain Monte Carlo (SGMCMC) has become a popular MCMC method that aims to be …
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Bayesian Computation for Variable Selection and Multivariate Forecasting in Dynamic Models
… dissertation presents techniques for efficient Bayesian computation in multivariate time series analysis. Computational scalability is a core focus of this work, and often rests on the decouple-recouple concept in which multivariate models are decoupled into univariate models for efficient …
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Prediction under uncertainty : from models for marine-terminating glaciers to Bayesian computation
… accurate dynamical model is available, computational limitations make it difficult to characterize uncertainties associated with the model's predictions. To address this prediction challenge, this thesis presents complementary developments in glaciology and in Bayesian computation.
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Calibrating high frequency trading data to agent based models using approximate Bayesian computation
We consider Sequential Monte Carlo Approximate Bayesian Computation (SMC ABC) as a method of calibration for the use of agent based models in market micro-structure. To date, there are no successful calibrations of agent based models to high frequency trading data. Here we test whether a more …
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Data conditioned simulation and inference
With the increasing power of personal computers, computational intensive statistical methods such as approximate Bayesian computation (ABC) are becoming an attractive and viable proposition to analyse complex statistical problems. There are three main aspects to ABC: • Proposing parameters. • …
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Calibrating a Latent Order Book Model to Market Data
… using likelihood-free methods, Approximate Bayesian Computation (ABC) and an iterative extension, Population Monte-Carlo ABC (PMC-ABC) as well as a Black-box approach using the Nelder-Mead algorithm. We show that in the diffusion limit, the master equation becomes the LOB reaction-diffusion …
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New model-based methods for non-differentiable optimization
… parameter of the probabilistic model in a Bayesian manner, and thus provides a proper way to determine the diversity in the population of the models. We provide theoretical justification on the convergence of this framework by showing that the posterior distribution of the parameter …
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Understanding and managing Frogeye Leaf Spot through network-based modeling in soybean
… FLS management in soybeans. Using Approximate Bayesian Computation, we estimated key epidemiological parameters and found that infection origin can shift the balance between transmission routes. Data analyses indicated that tillage and non-tillage plots did not differ significantly in fungal …
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Numerical approximation and parametric statistical inference of stochastic differential equations, with applications to finance
… techniques. By an application of approxiate Bayesian computation (ABC) we develop two sampling algorithms that are capable of producing high quality approximations to the posterior distribution of model parameters, without any need to evaluate model likelihoods.
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Forest dynamics at regional scales: predictive models constrained with inventory data
… inventories combined with improvements in computational methods mean that models that incorporate the climate dependency of demographic processes may be parameterised at regional scales. In Chapter One I outline historical approaches to modelling forest dynamics and present a discussion of …
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Essays in Political Economics and Networks
… can be estimated using a modified Approximate Bayesian computation method. We showcase our approach with three distinct empirical examples. The first example focuses on the legislative effectiveness of politicians in the 111th and 112th U.S. Congress. The second example looks at R&D …
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Bayesian Methods and Machine Learning in Astrophysics
This thesis is concerned with methods for Bayesian inference and their applications in astrophysics. We principally discuss two related themes: advances in nested sampling (Chapters 3 to 5), and Bayesian sparse reconstruction of signals from noisy data (Chapters 6 and 7). Nested sampling is a …
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The demography of Red Sea reef fishes since the Last Glacial Maximum
… sequencing data combined with an Approximate Bayesian Computation framework (including machine learning techniques) provided sufficient power to estimate population parameters for five reef fish species, Dascyllus abudafur, Dascyllus trimaculatus, Dascyllus marginatus, Pomacanthus maculosus, …
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Approximate Inference: New Visions
… confidence. Powered by the rules of probability, Bayesian inference is the gold standard method to perform coherent reasoning under uncertainty. It is generally believed that intelligent systems following the Bayesian approach can better incorporate uncertainty information for reliable decision …
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Scalable Estimation and Testing for Complex, High-Dimensional Data
… We introduce a wavelet-based approximate Bayesian computation approach that is likelihood-free and computationally scalable. This approach will be applied to two applications: estimating mutation rates of a generalized birth-death process based on fluctuation experimental data and …
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