Approximate Bayesian computation schemes for parameter inference of discrete stochastic models using simulated likelihood density.
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A continuous optimization approach for inferring parameters in mathematical models of regulatory networksStochastic modeling of biochemical systems with multistep reactions using state-dependent time delay.Reverse-engineering of gene networks for regulating early blood development from single-cell measurements.
P2860
Approximate Bayesian computation schemes for parameter inference of discrete stochastic models using simulated likelihood density.
description
2014 nî lūn-bûn
@nan
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
2014年论文
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2014年论文
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name
Approximate Bayesian computati ...... simulated likelihood density.
@en
type
label
Approximate Bayesian computati ...... simulated likelihood density.
@en
prefLabel
Approximate Bayesian computati ...... simulated likelihood density.
@en
P2860
P1433
P1476
Approximate Bayesian computati ...... simulated likelihood density.
@en
P2093
Qianqian Wu
P2860
P2888
P356
10.1186/1471-2105-15-S12-S3
P478
15 Suppl 12
P577
2014-11-06T00:00:00Z
P6179
1003061495