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A Bayesian Finite Mixture Model for Network-Telecommunication Data
Stockholm University, Faculty of Social Sciences, Department of Statistics.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 80 credits / 120 HE creditsStudent thesis
Abstract [en]

A data modeling procedure called Mixture model, is introduced beneficial to the characteristics of our data. Mixture models have been proved flexible and easy to use, a situation which can be confirmed from the majority of papers and books which have been published the last twenty years. The models are estimated using a Bayesian inference through an efficient Markov Chain Monte Carlo (MCMC) algorithm, known as Gibbs Sampling. The focus of the paper is on models for network-telecommunication lab data (not time dependent data) and on the valid predictions we can accomplish. We categorize our variables (based on their distribution) in three cases, a mixture of Normal distributions with known allocation, a mixture of Negative Binomial Distributions with known allocations and a mixture of Normal distributions with unknown allocation.

Place, publisher, year, edition, pages
2016. , p. 51
Keywords [en]
Mixture Model, Bayesian Inference, Markov Chain Monte Carlo, Gibbs Sampling, Network-Telecommunication Lab Data
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:su:diva-146039OAI: oai:DiVA.org:su-146039DiVA, id: diva2:1134848
External cooperation
Ericsson AB
Presentation
2016-06-03, 14:30 (English)
Supervisors
Examiners
Available from: 2018-02-09 Created: 2017-08-21 Last updated: 2018-02-09Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
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  • vancouver
  • Other style
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  • de-DE
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  • en-US
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  • nn-NB
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  • Other locale
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Output format
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