Approximate Bayesian computation: Difference between revisions

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| Python package for ABC and other likelihood-free inference schemes. Several state-of-the-art algorithms available. Provides quick way to integrate existing generative (from C++, R etc.), user-friendly parallelization using MPI or Spark and summary statistics learning (with neural network or linear regression).
| <ref>{{cite arXiv |title=ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation. |last1=Dutta |first1=R |last2=Schoengens |first2=M |last3=Pacchiardi |first3=L |last4=Ummadisingu |first4=A |last5=Widmer |first5=N |last6=Onnela |first6=J. P. |last7=Mira |first7=A
|year=2020|arxivclass=stat.CO |eprint=1711.04694 }}</ref>
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The suitability of individual software packages depends on the specific application at hand, the computer system environment, and the algorithms required.