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 16 of 16 for “"Distribution Estimation"”.
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Permeability Distribution Estimation Based on Semi-Analytical Reservoir Simulator
Estimation of rock permeability distribution can be realized by automatic history matching production data. The process of automatic history matching involves the minimization of an objective function which usually includes the sum of data mismatch part as well as the sum of prior model mismatch …
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The Truncated Cauchy Distribution: Estimation of Parameters and Application to Stock Returns
… in this dissertation is the existence and estimation of the parameters of a truncated Cauchy distribution. It is known that when a number of distributions with infinite support are truncated to a finite interval that the maximum likelihood estimator of the scale parameter fails to exist …
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Speech-Based Emotion Modelling and Mental Disorder Detection
… it is proposed to represent emotion as a distribution rather than a single class. Different emotion annotations provided by human annotators are treated as samples drawn from the emotion distribution. Evidential deep learning (EDL) is used to quantify the uncertainty in emotion …
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Learning with classical and quantum information constraints
… of physics in quantum computers. First, we study distribution learning and testing with local information constraints such as local differential privacy (LDP) and communication constraints. We derive a general lower-bound framework for interactive communication protocols. The techniques and ideas …
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Probability based scheduling to optimize sewer maintenance
… between blockages, is described by two-parameter distribution. Each pipe in the sewer system has characteristic parameters and distribution that is also utilized to simulate the operation of sewer system in the model. Fitting the parameters from historical database, estimated parameters are used …
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Value-add in technical analysis on the JSE Bond Market
… to more accurately estimate the underlying distribution of the time series, that is assumed to be normal in the standard methodology. It is shown that no additional benefit is derived from the alternative distribution estimation methods. Bollinger Bands make an assumption of stationarity on …
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Estimability of natural mortality within a statistical catch-at-age model: a framework and simulation study based on Gulf of Mexico red snapper
Estimation of natural mortality within statistical catch-at-age models has been relatively unsuccessful and is uncommon within stock assessments. The models I created estimated population-dynamics parameters, including natural mortality, through Metropolis-Hastings algorithms from Gulf of Mexico …
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Rethinking Algorithm Design for Modern Challenges in Data Science
… for mitigating corruptions in the context of distribution estimation, linear regression, and online learning. A distinctive feature of many of our results here is that they make minimal assumptions on the data-generating process. In certain situations however, data may be difficult to work …
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DEUM: a framework for an estimation of distribution algorithm based on Markov random fields.
Estimation of Distribution Algorithms (EDAs) belong to the class of population based optimisation algorithms. They are motivated by the idea of discovering and exploiting the interaction between variables in the solution. They estimate a probability distribution from population of solutions, and …
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Multivariate Markov networks for fitness modelling in an estimation of distribution algorithm.
… development in evolutionary computation is the Estimation of Distribution Algorithm (EDA) which replaces the traditional genetic reproduction operators (crossover and mutation) with the construction and sampling of a probabilistic model. While this can often represent a significant computational …
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Systems modeling of quantitative kinetic data identifies receptor tyrosine kinase-specific resistance mechanisms to MAPK pathway inhibition in cancer
… to any membrane bound protein. Parameter distribution estimation by fitting data to an integrative cellular model quantifies native RTK processes and enables the study of treatment induced mechanistic changes. It has been reported that triple negative breast cancer cell lines up-regulate …
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Light scattering: from ensembles to single particles
… By viewing the inverse problem of size distribution estimation within the Bayesian framework, a method for extracting an uncertainty quantified (UQ) estimate of the size distribution is presented. The technique is further generalized from a static inverse problem to a dynamic one, …
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SET-UP OF INNOVATIVE EXPERIMENTAL METODOLOGIES FOR THE ATMOSPHERIC AEROSOL CHARACTERISATION AND SOURCE APPORTIONMENT
… of the University of Genoa for elemental size distribution determination (see next paragraph); 2)application of the PMF (Positive Matrix Factorization) receptor model to a 4-hour resolved dataset already available. In this work, PMF resolved seven main sources affecting the Milan urban area …
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Probabilistic motion planning and optimization incorporating chance constraints
… for chance constraints through state probability distribution and collision probability estimation. Based on the deterministic Chekov planner, p-Chekov incorporates a linear-quadratic Gaussian motion planning (LQG-MP) approach into robot state probability distribution estimation, applies …
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Recent Advances in Bayesian Copula Models for Mixed Data and Quantile Regression
… Copula models link arbitrary univariate marginal distributions under a multivariate dependence structure to define a valid joint distribution for a random vector. By estimating the joint distribution of a multivariate random vector, we are granted access to a myriad of information, from marginal …
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Deep Time: Deep Learning Extensions to Time Series Factor Analysis with Applications to Uncertainty Quantification in Economic and Financial Modeling
This thesis establishes methods to quantify and explain uncertainty through high-order moments in time series data, along with first principal-based improvements on the standard autoencoder and variational autoencoder. While the first-principal improvements on the standard variational autoencoder …