Welcome to pyABC’s documentation!¶
- Release
0.10.1
- 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
- Examples
- Quickstart
- Parameter inference
- 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
- Aggregating and weighting diverse data
- Script based external simulators
- Data plots
- Measurement noise assessment
- PEtab import
- Download the examples as notebooks
About
Developer's guide
API reference
- API reference
- Parallel Approximate Bayesian Computation - Sequential Monte Carlo
- Distance functions
- Acceptor
- Models
- Epsilon threshold scheduling strategies
- Data store
- Transitions (Perturbation Kernels)
- Population strategy
- Multi-core and Distributed Sampling
- Parameters
- Particles and Populations
- Random variables
- Parallel job execution on SGE like environments
- External simulators
- Visualizations
- Weighted Statistics