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[ascl:2309.006] CoLFI: Cosmological Likelihood-Free Inference

CoLFI (Cosmological Likelihood-Free Inference) estimates parameters directly from the observational data sets using neural density estimators (NDEs); it is a fully ANN-based framework that differs from the Bayesian inference. The package contains three NDEs that are used to estimate parameters: an artificial neural network (ANN), a mixture density network (MDN), and a mixture neural network (MNN). CoLFI can learn the conditional probability density using samples generated by models, and the posterior distribution can be obtained for given observational data.

Code site:
https://github.com/Guo-Jian-Wang/colfi
Described in:
https://ui.adsabs.harvard.edu/abs/2023ApJS..268....7W
Bibcode:
2023ascl.soft09006W
Preferred citation method:

https://ui.adsabs.harvard.edu/abs/2023ApJS..268....7W ; please see additional citation information at https://github.com/Guo-Jian-Wang/colfi#attribution


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ascl:2309.006
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