pyABC: Efficient and robust easy-to-use approximate Bayesian computation

Understand

The Python package pyABC provides a framework for approximate Bayesian computation (ABC), a likelihood-free parameter inference method popular in many research areas.

• At its core, it implements a sequential Monte-Carlo (SMC) scheme, with various algorithms to adapt to the problem structure and automatically tune hyperparameters.
• To scale to computationally expensive problems, it provides efficient parallelization strategies for multi-core and distributed systems.
• The package is highly modular and designed to be easily usable.