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Using Active Learning for Speeding up Calibration in Simulation Models
BACKGROUND: Most cancer simulation models include unobservable parameters that determine the disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality and their values are typically estimated via lengthy calibration proced...
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| Publicado no: | Med Decis Making |
|---|---|
| Main Authors: | , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2015
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4833719/ https://ncbi.nlm.nih.gov/pubmed/26471190 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1177/0272989X15611359 |
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