Two-dimensional Gibbs sampling method based on layered Bayesian model
The invention discloses a two-dimensional Gibbs sampling method based on a hierarchical Bayesian model, and the method comprises the steps: constructing a P function which only needs to calculate two parameters according to a known hierarchical Bayesian model, then obtaining a target parameter through two-dimensional Gibbs sampling, and finally giving an individualized administration dosage. As only two parameters CL and V need to be calculated, the calculation steps are convenient and simple, the calculation efficiency is greatly improved, function calculation is not needed, in the sampling step, only the average value of CL and V in a group needs to be selected as an initial value and then substituted into a P function, and the calculation efficiency is greatly improved. Therefore, the distribution of the sampled samples is closer to the target distribution, and the sampling efficiency is further improved..
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Patent| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Europäisches Patentamt - (2023) vom: 28. März Zur Gesamtaufnahme - year:2023 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
LI GUODONG [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
Sonstige Themen: |
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| Anmerkungen: |
Source: www.epo.org (no modifications made), First posted: 2023-03-28, Last update posted on www.tib.eu: 2023-06-27, Last updated: 2023-06-30 |
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| Patentnummer: |
CN115862880 |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
EPA017295599 |
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| 245 | 1 | 0 | |a Two-dimensional Gibbs sampling method based on layered Bayesian model |
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| 500 | |a Source: www.epo.org (no modifications made), First posted: 2023-03-28, Last update posted on www.tib.eu: 2023-06-27, Last updated: 2023-06-30 | ||
| 520 | |a The invention discloses a two-dimensional Gibbs sampling method based on a hierarchical Bayesian model, and the method comprises the steps: constructing a P function which only needs to calculate two parameters according to a known hierarchical Bayesian model, then obtaining a target parameter through two-dimensional Gibbs sampling, and finally giving an individualized administration dosage. As only two parameters CL and V need to be calculated, the calculation steps are convenient and simple, the calculation efficiency is greatly improved, function calculation is not needed, in the sampling step, only the average value of CL and V in a group needs to be selected as an initial value and then substituted into a P function, and the calculation efficiency is greatly improved. Therefore, the distribution of the sampled samples is closer to the target distribution, and the sampling efficiency is further improved. | ||
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| 700 | 0 | |a MA PENGCHENG |4 aut | |
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| 700 | 0 | |a HUANG HUITING |4 aut | |
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