Welcome to pyABC’s documentation!
- Release
0.11.7
- Source code
pyABC is a framework for distributed, likelihood-free inference. That means, if you have a model and some data and want to know the posterior distribution over the model parameters, i.e. you want to know with which probability which parameters explain the observed data, then pyABC might be for you.
All you need is some way to numerically draw samples from the model, given the model parameters. pyABC “inverts” the model for you and tells you which parameters were well matching and which ones not. You do not need to analytically calculate the likelihood function.
pyABC runs efficiently on multi-core machines and distributed cluster setups. It is easy to use and flexibly extensible.
If you use it in your work, you can cite the paper:
Emmanuel Klinger, Dennis Rickert, Jan Hasenauer; pyABC: distributed, likelihood-free inference; Bioinformatics 2018; https://doi.org/10.1093/bioinformatics/bty361
User's guide
- What is pyABC about?
- Installation und Upgrading
- Examples
- Parameter inference
- Model selection
- Early stopping of model simulations
- Resuming stored ABC runs
- Using R with pyABC
- Ordinary differential equations: Conversion reaction
- Markov jump process: Reaction network
- Multi-scale model: Tumor spheroid growth
- Stochastic differential equation: Ion channel noise in Hodgkin-Huxley neurons
- Adaptive distances
- Informative distances and summary statistics
- Aggregating and weighting diverse data
- Wasserstein distances
- Script based external simulators
- Data plots
- Measurement noise and exact inference
- PEtab import and yaml2sbml
- PEtab application example
- Discrete parameters
- Look-ahead sampling
- Download the examples as notebooks
- Parallel and Distributed Sampling
- Data store
- Visualization and analysis
About
Developer's guide
API reference