TY - JOUR AU - Dutta, Ritabrata AU - Schoengens, Marcel AU - Pacchiardi, Lorenzo AU - Ummadisingu, Avinash AU - Widmer, Nicole AU - Künzli, Pierre AU - Onnela, Jukka-Pekka AU - Mira, Antonietta PY - 2021/11/30 Y2 - 2024/03/29 TI - ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation JF - Journal of Statistical Software JA - J. Stat. Soft. VL - 100 IS - 7 SE - Articles DO - 10.18637/jss.v100.i07 UR - https://www.jstatsoft.org/index.php/jss/article/view/v100i07 SP - 1 - 38 AB - <p>ABCpy is a highly modular scientific library for approximate Bayesian computation (ABC) written in Python. The main contribution of this paper is to document a software engineering effort that enables domain scientists to easily apply ABC to their research without being ABC experts; using ABCpy they can easily run large parallel simulations without much knowledge about parallelization. Further, ABCpy enables ABC experts to easily develop new inference schemes and evaluate them in a standardized environment and to extend the library with new algorithms. These benefits come mainly from the modularity of ABCpy. We give an overview of the design of ABCpy and provide a performance evaluation concentrating on parallelization. This points us towards the inherent imbalance in some of the ABC algorithms. We develop a dynamic scheduling MPI implementation to mitigate this issue and evaluate the various ABC algorithms according to their adaptability towards high-performance computing.</p> ER -