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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
171

Comparison of Prediction Intervals for the Gumbel Distribution

Fang, Lin 06 1900 (has links)
<p> The problem of obtaining a prediction interval at specified confidence level to contain k future observations from the Gumbel distribution, based on an observed sample from the same distribution, is considered. An existing method due to Hahn, which is originally valid for the normal, is adapted to the Gumbel case. Motivated by the equivalence between Hahn's prediction intervals and Bayesian predictive intervals for the normal, we develop Bayesian predictive intervals for the Gumbel in the case where the scale parameter b is both known and unknown. Furthermore, we perform comparison of Hahn's and Bayesian intervals. We find that the Bayesian is better in the b known case, while Hahn and Bayes perform about the same in the other case when b is unknown. We then consider the maximum of the Hahn's and Bayesian predicted lower limits which is shown to be a better predictor when b is unknown. All the discussions are based on Monte Carlo simulations. In the end, the results are applied to Ontario Power Generation data on feeder thicknesses.</p> / Thesis / Master of Science (MSc)
172

Deep Learning for Taxonomy Prediction

Ramesh, Shreyas 04 June 2019 (has links)
The last decade has seen great advances in Next-Generation Sequencing technologies, and, as a result, there has been a rise in the number of genomes sequenced each year. In 2017, there were as many as 10,000 new organisms sequenced and added into the RefSeq Database. Taxonomy prediction is a science involving the hierarchical classification of DNA fragments up to the rank species. In this research, we introduce Predicting Linked Organisms, Plinko, for short. Plinko is a fully-functioning, state-of-the-art predictive system that accurately captures DNA - Taxonomy relationships where other state-of-the-art algorithms falter. Plinko leverages multi-view convolutional neural networks and the pre-defined taxonomy tree structure to improve multi-level taxonomy prediction. In the Plinko strategy, each network takes advantage of different word usage patterns corresponding to different levels of evolutionary divergence. Plinko has the advantages of relatively low storage, GPGPU parallel training and inference, making the solution portable, and scalable with anticipated genome database growth. To the best of our knowledge, Plinko is the first to use multi-view convolutional neural networks as the core algorithm in a compositional,alignment-free approach to taxonomy prediction. / Master of Science / Taxonomy prediction is a science involving the hierarchical classification of DNA fragments up to the rank species. Given species diversity on Earth, taxonomy prediction gets challenging with (i) increasing number of species (labels) to classify and (ii) decreasing input (DNA) size. In this research, we introduce Predicting Linked Organisms, Plinko, for short. Plinko is a fully-functioning, state-of-the-art predictive system that accurately captures DNA - Taxonomy relationships where other state-of-the-art algorithms falter. Three major challenges in taxonomy prediction are (i) large dataset sizes (order of 109 sequences) (ii) large label spaces (order of 103 labels) and (iii) low resolution inputs (100 base pairs or less). Plinko leverages multi-view convolutional neural networks and the pre-defined taxonomy tree structure to improve multi-level taxonomy prediction for hard to classify sequences under the three conditions stated above. Plinko has the advantage of relatively low storage footprint, making the solution portable, and scalable with anticipated genome database growth. To the best of our knowledge, Plinko is the first to use multi-view convolutional neural networks as the core algorithm in a compositional, alignment-free approach to taxonomy prediction.
173

Predicting college success from mental test scores and cumulative scholastic records

Irwin, Ralph Alexander. January 1929 (has links)
Call number: LD2668 .T4 1929 I71
174

The prediction of high school scholarship from junior high school grades and mental tests

Howard, Charles Wilber. January 1929 (has links)
Call number: LD2668 .T4 1929 H64
175

The size of high school as a factor in college success

Cragun, Orville Robinson. January 1931 (has links)
Call number: LD2668 .T4 1931 C71
176

The value of homogeneity in predicting college success from high school grades

Adell, Harry Enoch. January 1931 (has links)
Call number: LD2668 .T4 1931 A31
177

The effect of college entrance delay on college grades

Stuart, Hilmar Clinton. January 1941 (has links)
LD2668 .T4 1941 S76 / Master of Science
178

The prediction of academic attrition and success through discriminate function analysis

Talley, Boyd Gayle. January 1958 (has links)
Call number: LD2668 .T4 1958 T34
179

Geometry grades: a predictor of achievement in physics and chemistry in Topeka public schools, Topeka, Kansas

Dirks, Roger Lee. January 1966 (has links)
LD2668 .T4 1966 D599 / Master of Science
180

Prediction of college success from high school grades and intelligence test scores

Hochuli, Alma. January 1928 (has links)
Call number: LD2668 .T4 1928 H61

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