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 129 for “"Bayesian statistics"”.
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Bayesian statistics for fishery stock assessment and management
This work is about the use of Bayesian statistics in fishery stock assessment and management. Multidimensional posterior distributions replace classical parameter estimation in surplus-production and delay-difference models. The maximization of expected utilities replaces the estimation of optimal …
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Machine Learning and Bayesian Statistics for Seismic Compressive Sensing
… data. We propose to use algorithms from the Bayesian statistics and machine learning field that allow the construction of models using probability distributions over random variables. This allows the modelling of sparsity and provides flexibility by adding or removing basis functions from the …
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Bayes meets Bach: applications of Bayesian statistics to audio restoration
… nonlinearly degraded recordings is tackled in a Bayesian context, considering both autoregressive models and sparsity in the DCT domain for the original signal, as well as through a deterministic solution also based on sparsity; for the suppression of long pulses, a parametric approach is …
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A Bayesian statistics approach to updating finite element models with frequency response data
… qualified data, which is then used in a Bayesian statistics regression formulation to update the finite element model. The Bayesian formulation allows the analyst to incorporate engineering judgment (in the form of prior knowledge) into the analysis and helps ensure that reasonable and …
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The application of Bayesian statistics and maximum entropy to Ion beam analysis techniques
… hoc modifications to solve different problems. Bayesian Statistics has been proved to be the only consistent method for solving inverse problems of the type where the information is expressed in terms of probability distributions. This dissertation presents results of applying the Bayesian …
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Weighting protein ensembles with Bayesian statistics and small-angle X-ray scattering data
… set of experimental observables. The Variational Bayesian Weighting program uses Bayesian statistics to fit conformational ensembles, and in doing so also quantifies the uncertainty in the underlying ensemble. The present work sought to introduce new functionality to this program, allowing it to …
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Bayesian algorithms for automated isotope identification
… We propose a new algorithm using Bayesian statistics that uses peak positions and areas to identify the source while allowing for calibration drift and shielding.
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Application of Machine Learning to Mapping and Simulating Gene Regulatory Networks
… methods of applying modernmachine learning and Bayesian statistics in the quantitative and qualitative modeling of gene regulatory networks using high-throughput gene expression data. A semi-parametric Bayesian model based on random forest is developed to infer quantitative aspects of gene …
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The use of aminoglycoside antibiotic therapy in neutropaenic patients with haematological disease
… in this population and then reports the use of a Bayesian statistics based predictive model to implement and manage therapy in 10 patients. A review of the literature on aminoglycoside Pharmacology and clinical use is essential to determine therapeutic guidelines for this population. …
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Computability, inference and modeling in probabilistic programming
… and noise, both of which are common in Bayesian hierarchical modeling. This theoretical work bears on the development of probabilistic programming languages (which enable the specification of complex probabilistic models) and their implementations (which can be used to perform Bayesian …
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A UNIFORMLY MOST POWERFUL TEST FOR THE MEAN OF A BETA DISTRIBUTION
… multivariate analysis of variance (MANOVA) and Bayesian statistics. It is a flexible distribution that can account for many different characteristics of real data. To our surprise, there has been very little work or discussion on performing statistical hypothesis testing for the mean when it is …
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Bayesian risk management : "Frequency does not make you smarter"
Within our research group Bayesian Risk Solutions we have coined the idea of a Bayesian Risk Management (BRM). It claims (1) a more transparent and diligent data analysis as well as (2)an open-minded incorporation of human expertise in risk management. In this dissertation we formulize a framework …
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Probabilistic Models on Fibre Bundles
… operators, dimension reduction, regression and Bayesian statistics.</p>
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Discriminative, generative, and imitative learning
… priors over the joint space of variables. Bayesian networks and Bayesian statistics provide a rich and flexible language for specifying this knowledge and subsequently refining it with data and observations. The final result is a distribution that is a good generator of novel exemplars. …
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Distributed Localization for Wireless Distributed Networks in Indoor Environments
… using either a Euclidean distance algorithm, Bayesian statistics, or neural networks. With large service areas and, subsequently, large radio maps, one mobile computer may not have the adequate resources to locally compute a user's position. Wireless distributed computing provides a means for …
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Assessment of a Space Shuttle trajectory evaluation system (DOLILU II)
… conditions needed to test the system.;We used a Bayesian statistical framework for reliability assessment. Bayesian statistics uses knowledge about the system to be incorporated into the reliability model before testing. DOLILU II has been operational for nearly five years. We use this …
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Evaluating The Efficiency of Markov Chain Monte Carlo Algorithms
… to converge to a stationary distribution. In Bayesian statistics, MCMC is used to obtain samples from a posterior distribution for inference. To ensure the accuracy of estimates using MCMC samples, the convergence to the stationary distribution of an MCMC algorithm has to be checked. As …
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Electrophysiology, agent-based modeling and inverse optimal control applications in neuroethology
… agent-based modeling, classical conditioning, Bayesian statistics and control theory to investigate foraging decisions of the Pleurobranchaea, as it integrates sensation, internal state and learning mechanisms.
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A short-term ensemble wind-speed forecasting system for wind power applications
… physics options. The PDF was calibrated using Bayesian Model Averaging (BMA) where the individual forecasts were weighted according to their performance. This combination of a mesoscale numerical weather prediction ensemble system and Bayesian statistics allowed for both accurate prediction of …
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Estimating value at risk and expected shortfall: a kalman filter approach
… as a stochastic process, the Kalman filter uses Bayesian statistics to forecast unobservable data by identifying underlying patterns required to predict future values. Back-testing results (in which the number of times VaR or ES forecasted too low a value to cover the following day's market loss …
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