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| Paper: |
idl_emcee: IDL Implementation of the MCMC Ensemble Sampler |
| Volume: |
538, ADASS XXXII |
| Page: |
385 |
| Authors: |
Danehkar, Ashkbiz |
| DOI: |
10.26624/BIVU8108 |
| Abstract: |
Ensemble samplers can be used in statistical analyses of observations containing instrumental and measurement uncertainties. To facilitate this approach in the Interactive Data Language (IDL), we present idl_emcee, an implementation of the affine-invariant Markov chain Monte Carlo (MCMC) ensemble sampler based on the algorithm prescribed by Goodman & Weare (2010). This IDL library allows us to create samplers for the probability densities of new data generated by a user-defined function using prior information, which can be employed to handle uncertainties in empirical studies of observational data and find confidence levels of new data in computations. This package has recently been used to propagate uncertainties of measured fluxes into plasma diagnostics and abundance analyses of ionized gaseous nebulae. It is being built for IDL, but it has also been tested in the GNU Data Language (GDL), an open-source IDL compiler that can be deployed on Jupyter notebooks. |
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