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  • Statistical analysis and modelling of various phenomena are well established in nowadays industrial practice. However, the traditional approaches neglecting the true properties of the phenomena still dominate. Among others, this includes also the cases when a variable with bounded range is analyzed using probabilistic distributions with unbounded domain. Since many of those variables nearly fulfill the basic conditions imposed by the chosen distribution, the properties of used statistical models are violated rather rarely. Still, there are numerous cases, when inference with distributions with unbounded domain may lead to absurd conclusions. The paper addresses this issue from the Bayesian viewpoint. It briefly discusses suitable distributions and inferential methods overcoming the emerging computational issues.
  • Statistical analysis and modelling of various phenomena are well established in nowadays industrial practice. However, the traditional approaches neglecting the true properties of the phenomena still dominate. Among others, this includes also the cases when a variable with bounded range is analyzed using probabilistic distributions with unbounded domain. Since many of those variables nearly fulfill the basic conditions imposed by the chosen distribution, the properties of used statistical models are violated rather rarely. Still, there are numerous cases, when inference with distributions with unbounded domain may lead to absurd conclusions. The paper addresses this issue from the Bayesian viewpoint. It briefly discusses suitable distributions and inferential methods overcoming the emerging computational issues. (en)
Title
  • Overview of Bounded Support Distributions and Methods for Bayesian Treatment of Industrial Data
  • Overview of Bounded Support Distributions and Methods for Bayesian Treatment of Industrial Data (en)
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  • Overview of Bounded Support Distributions and Methods for Bayesian Treatment of Industrial Data
  • Overview of Bounded Support Distributions and Methods for Bayesian Treatment of Industrial Data (en)
skos:notation
  • RIV/67985556:_____/13:00394473!RIV14-MSM-67985556
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  • I, P(7D12004)
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  • 95026
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  • RIV/67985556:_____/13:00394473
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  • statistical analysis; Bayesian analysis; Truncated distributions (en)
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  • [709279DAEA81]
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  • Reykjavík
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  • Portugalsko
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  • Proceedings of the 10th international conference on informatics in control, automation and robotics (ICINCO 2013)
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  • Dedecius, Kamil
  • Ettler, P.
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number of pages
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  • INSTICC – Institute for Systems and Technologies of Information, Control and Communication
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  • 978-989-8565-70-9
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