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DC Field | Value | Language |
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dc.date.accessioned | 2019-03-11T13:52:47Z | - |
dc.date.available | 2019-03-11T13:52:47Z | - |
dc.date.issued | 2010 | - |
dc.identifier.citation | Tua, A., & Adami, K. Z. (2010). Bayesian computational methods: a comparison. arXiv preprint arXiv:1003.3357v2, 1-8. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar//handle/123456789/41123 | - |
dc.description.abstract | This paper focuses on utilizing two different Bayesian methods to deal with a variety of toy problems which occur in data analysis. In particular we implement the Variational Bayesian and Nested Sampling methods to tackle the problems of polynomial selection and Gaussian Mixture Models, comparing the algorithms in terms of processing speed and accuracy. In the problems tackled here it is the Variational Bayesian algorithms which are the faster though both results give similar results. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Cornell University | en_GB |
dc.rights | info:eu-repo/semantics/openAccess | en_GB |
dc.subject | Bayesian statistical decision theory | en_GB |
dc.subject | Variational inequalities (Mathematics) | en_GB |
dc.subject | Gaussian processes -- Data processing | en_GB |
dc.title | Bayesian computational methods : a comparison | en_GB |
dc.type | article | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder | en_GB |
dc.description.reviewed | N/A | en_GB |
dc.contributor.creator | Tua, A. | - |
dc.contributor.creator | Zarb Adami, Kristian | - |
Appears in Collections: | Scholarly Works - FacSciPhy |
Files in This Item:
File | Description | Size | Format | |
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Bayesian_computational_methods.pdf | 621.14 kB | Adobe PDF | View/Open |
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