Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Multivariate Conditional Distribution Estimation and Analysis
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
Abstract [en]

The goals of this thesis were to implement different methods for estimating conditional distributions from data and to evaluate the performance of these methods on data sets with different characteristics. The methods were implemented in C++ and several existing software libraries were also used. Tests were run on artificially generated data sets and on some real-world data sets. The accuracy, run time and memory usage of the methods was measured. Based on the results the natural or smoothing spline methods or the k-nearest neighbors method would potentially be a good first choice to apply to a data set if not much is known about it. In general the wavelet method did not seem to perform particularly well. The noisy-OR method could be a faster and possibly more accurateĀ  alternative to the popular logistic regression in certain cases.

Place, publisher, year, edition, pages
2014.
Series
IT, 14 062
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-234657OAI: oai:DiVA.org:uu-234657DiVA: diva2:757398
Educational program
Master Programme in Computer Science
Supervisors
Examiners
Available from: 2014-10-22 Created: 2014-10-22 Last updated: 2014-10-22Bibliographically approved

Open Access in DiVA

fulltext(2020 kB)