Distribution-free stochastic model updating with staircase density functions Article Swipe
M. Kitahara
,
Tatsumi Kitahara
,
Sifeng Bi
,
Mario F. Broggi
,
Michael Beer
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1201/9781003323020-81
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1201/9781003323020-81
In stochastic model updating, hybrid uncertainties are typically characterized by the distributional p-box. It assigns a certain probability distribution to model parameters and assumes its hyper-parameters as interval values. Thus, regardless of the updating method employed, the distribution family needs to be known a priori to parameterize the distribution. Meanwhile, a novel class of the random variable, called staircase random variable, can discretely approximate a wide range of distributions by solving moment-matching optimization problem. The first author and his co-workers have recently developed a distribution-free stochastic updating framework, in which model parameters are considered as staircase random variables and their hyper-parameters are inferred in a Bayesian fashion. This framework can explore an optimal distribution from a broad range of potential distributions according to the available data. This study aims to further demonstrate the capability of this framework through a simple numerical example with a parameter following various types of distributions.
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Distribution-free stochastic model updating with staircase density functionsWork title
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book-chapterOpenAlex work type
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enPrimary language
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2023Year of publication
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2023-06-28Full publication date if available
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M. Kitahara, Tatsumi Kitahara, Sifeng Bi, Mario F. Broggi, Michael BeerList of authors in order
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Distribution (mathematics), Statistical physics, Mathematics, Computer science, Mathematical analysis, PhysicsTop concepts (fields/topics) attached by OpenAlex
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| cited_by_percentile_year | |
| countries_distinct_count | 3 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.33767495 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |