Filtered Gibbs sampler for estimating blocking in product form networks Article Swipe
Lachlan L. H. Andrew
,
Felisa J. Vázquez-Abad
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.25916/sut.26286070
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.25916/sut.26286070
The filtered Gibbs sampler (FGS) is a very simple yet efficient method of performance evaluation for a class of communication networks. This paper confirms that the FGS is considerably more efficient than the standard Gibbs sampler. It also shows that estimates of the accuracy of the results can be excessively optimistic, and presents a more conservative estimate.
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Metadata
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- article
- Language
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- Landing Page
- http://hdl.handle.net/1959.3/44408
- OA Status
- green
- Cited By
- 2
- References
- 21
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W1504879696
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W1504879696Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.25916/sut.26286070Digital Object Identifier
- Title
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Filtered Gibbs sampler for estimating blocking in product form networksWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
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Lachlan L. H. Andrew, Felisa J. Vázquez-AbadList of authors in order
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https://hdl.handle.net/1959.3/44408Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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Gibbs sampling, Blocking (statistics), Computer science, Channel (broadcasting), Product (mathematics), Simple (philosophy), Class (philosophy), Mathematical optimization, Algorithm, Mathematics, Artificial intelligence, Telecommunications, Computer network, Epistemology, Geometry, Bayesian probability, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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21Number of works referenced by this work
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.and | 51 |
| abstract_inverted_index.can | 47 |
| abstract_inverted_index.for | 15 |
| abstract_inverted_index.the | 25, 32, 42, 45 |
| abstract_inverted_index.yet | 9 |
| abstract_inverted_index.This | 21 |
| abstract_inverted_index.also | 37 |
| abstract_inverted_index.more | 29, 54 |
| abstract_inverted_index.than | 31 |
| abstract_inverted_index.that | 24, 39 |
| abstract_inverted_index.very | 7 |
| abstract_inverted_index.(FGS) | 4 |
| abstract_inverted_index.Gibbs | 2, 34 |
| abstract_inverted_index.class | 17 |
| abstract_inverted_index.paper | 22 |
| abstract_inverted_index.shows | 38 |
| abstract_inverted_index.method | 11 |
| abstract_inverted_index.simple | 8 |
| abstract_inverted_index.results | 46 |
| abstract_inverted_index.sampler | 3 |
| abstract_inverted_index.accuracy | 43 |
| abstract_inverted_index.confirms | 23 |
| abstract_inverted_index.filtered | 1 |
| abstract_inverted_index.presents | 52 |
| abstract_inverted_index.sampler. | 35 |
| abstract_inverted_index.standard | 33 |
| abstract_inverted_index.efficient | 10, 30 |
| abstract_inverted_index.estimate. | 56 |
| abstract_inverted_index.estimates | 40 |
| abstract_inverted_index.networks. | 20 |
| abstract_inverted_index.evaluation | 14 |
| abstract_inverted_index.excessively | 49 |
| abstract_inverted_index.optimistic, | 50 |
| abstract_inverted_index.performance | 13 |
| abstract_inverted_index.conservative | 55 |
| abstract_inverted_index.considerably | 28 |
| abstract_inverted_index.communication | 19 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile |