A practical guide to pseudo-marginal methods for computational inference in systems biology
For many stochastic models of interest in systems biology, such as those describing biochemical reaction networks, exact quantification of parameter uncertainty through statistical inference is intractable. Likelihood-free computational inference techniques enable parameter inference when the likeli...
Huvudupphovsmän: | Warne, DJ, Baker, RE, Simpson, MJ |
---|---|
Materialtyp: | Journal article |
Språk: | English |
Publicerad: |
Elsevier
2020
|
Liknande verk
Liknande verk
-
Rapid Bayesian inference for expensive stochastic models
av: Warne, DJ, et al.
Publicerad: (2021) -
Identifiability analysis for stochastic differential equation models in systems biology
av: Browning, AP, et al.
Publicerad: (2020) -
Parameter identifiability and model selection for sigmoid population growth models
av: Simpson, MJ, et al.
Publicerad: (2021) -
Multifidelity multilevel Monte Carlo to accelerate approximate Bayesian parameter inference for partially observed stochastic processes
av: Warne, D, et al.
Publicerad: (2022) -
Using experimental data and information criteria to guide model selection for reaction-diffusion problems in mathematical biology
av: Warne, D, et al.
Publicerad: (2019)