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 21 for “"statistical knowledge"”.
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An exploratory study of student attitudes toward statistics and their retention of statistical concepts
… with the comprehension and retention of statistical knowledge in Baccalaureate Psychology students. The criterion variable was statistical competency, which was measured in five subdomains: basic concepts, descriptives, correlation/regression, hypothesis testing, and inferential …
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An exploratory study of student attitudes toward statistics and their retention of statistical concepts
… with the comprehension and retention of statistical knowledge in Baccalaureate Psychology students. The criterion variable was statistical competency, which was measured in five subdomains: basic concepts, descriptives, correlation/regression, hypothesis testing, and inferential …
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Learning from one example in machine vision by sharing probability densities
… by the application of previously learned statistical knowledge to a new setting. This thesis presents an approach to acquiring knowledge in one setting and using it in another. Specifically, we develop probability densities over common image changes. Given a single image of a new object …
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<em>CausalModels</em>: An R Library for Estimating Causal Effects
<p>Free and open source software for statistical modeling and machine learning have advanced productivity in data science significantly. Packages such as <em>SciPy </em>in Python and <em>caret </em>in R provide fundamental tools for statistical modeling and machine learning in the two most popular …
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Failure to Reject the p-value is Not the Same as Accepting it: The Development, Validation, and Administration of the KPVMI Instrument
The purpose of this study was to investigate on a national scale the baseline level of p-value fluency of future researchers (i.e., doctoral students). To that end, two research questions were investigated. The first research question, Can a sufficiently reliable and valid measure of p-value …
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Democratizing data science through interactive curation of ML pipelines
Statistical knowledge and domain expertise are key to extract actionable insights out of data, yet such skills rarely coexist together. In Machine Learning, high-quality results are only attainable via mindful data preprocessing, hyperparameter tuning and model selection. Domain experts are often …
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An experimental and theoretical tool for studying the language of geometric concepts
… Shannon's insight of accessing our implicit statistical knowledge of the structure of a language by converting it to a reduced text form, through a prediction experiment. I generalize Shannon's experiment design to make it applicable for a wide variety of languages, beyond just text-based, …
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Semantic Image Interpretation - Integration of Numerical Data and Logical Knowledge for Cognitive Vision
… methods for SII. Both methods exploit background knowledge, in the form of logical constraints of a knowledge base, about the domain of the images. The first method formalizes the SII as the extraction of a partial model of a knowledge base. Partial models are built with a clustering and reasoning …
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A tool for hemodynamic data analysis
… signals. HemDAT uses signal processing and statistical knowledge to provide clinical researchers a tool that can help develop a better understanding of how brain injury occurs in premature newborns. HemDAT is capable of processing and navigating large data sets of blood pressure and cerebral …
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A model for predicting bacteria concentrations in runoff from agricultural lands
… resulting from previous modeling efforts with statistical knowledge concerning rainfall events and temperature variation. Model output is in the form of monthly maximum and minimum log bacteria concentrations of runoff resulting from a storm assumed to occur immediately after manure is applied …
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Resource management in wireless heterogeneous networks: an optimization perspective
… algorithm. The proposed algorithm can use the statistical knowledge instead of actual channel values and is guaranteed to converge to the set of stationary points of the stochastic sum-rate maximization problem. We further generalize our stochastic method to a cross layer framework for jointly …
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Computational and statistical approaches to optical spectroscopy
… challenges by combining modern computational and statistical techniques with physical domain knowledge. In particular, we focus on three aspects where computational or statistical knowledge have either enabled realization of a new instrument-with a compact form factor yet still maintaining a …
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Physical Layer Security for Wireless Position Location in the Presence of Location Spoofing
… which requires neither prior environmental nor statistical knowledge. This is accomplished by exploiting the bilateral behavior of a hybrid framework using two received signal strength (RSS) based location estimators. We show that the resulting approach is effective at detecting attacks with the …
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Anatomizing the social body: representing the plague in London, 1665
… of representation utilized visual, textual, and statistical elements in 'anatomizing’ the spaces of the City during the outbreak, and the social processes brought into play by the presence of the disease in the urban centre. I argue that this broadsheet attempted to create an ordered and …
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Comparing Schools: From Value Added to Sound Policy
… SSC lacks transparency and requires specialized statistical knowledge. SSC is promising for exhibiting stability, fairness, and transparency, but further investigation is needed to determine its validity and proper interpretation in comparison to other VAMs.</p>
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Computational imaging and inverse techniques for high-resolution and instantaneous spectral imaging
… framework to incorporate the additional prior statistical knowledge of the targeted objects. Computationally efficient algorithms are then designed to solve the resulting nonlinear optimization problems. In addition to the development of each technique, Bayesian Cramer-Rao bounds are also …
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Online advertisements and multi-armed bandits
… strategy which can be implemented without any statistical knowledge of bids, valuations, and query arrival processes. The key idea is to use stochastic approximation techniques to automatically track long-term averages. Next, we consider multi-armed bandits with budgets, modeling how ad …
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Speech recognition with probabilistic transcriptions and end-to-end systems using deep learning
… we demonstrate that it is possible to borrow statistical knowledge of acoustics from a variety of other well-resourced languages to learn the parameters of a the DNN in the target under-resourced language. In particular, we use well-resourced languages as cross-entropy regularizers to improve …
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Neural networks for the prediction of chaos and turbulence
… before the events occur. Third, we analyse the statistical prediction of extreme events. By training the networks with datasets that contain non-converged statistics, we show that the networks are able to extrapolate the flow's long-term statistics. In other words, the networks are able to …
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Lead poisoning: with special reference to renal and vascular symptoms
… Newcastle-on-Tyne has done much to increase our knowledge in recent years, and to bring industrial lead poisoning into prominence. Among other writers in the English language may be mentioned Goadby and Legge in this country, and Alice Hamilton in America. The legislature of almost all civilised …
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