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Probabilistic Inference on Noisy Time Series
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What is Pints?

Pints (Probabilistic Inference on Noisy Time-Series) is a framework for optimisation and bayesian inference problems with ODE models of noisy time-series, such as arise in electrochemistry and cardiac electrophysiology.

Using Pints

To use a model with Pints, you need to make sure it extends the ForwardModel interface, which has just two methods:

n_parameters() --> Returns the dimension of the parameter space.

simulate(parameters, times) --> Returns a vector of model evaluations at
                                the given times, using the given parameters

If your model implements these methods - or you can write a wrapper class that does - you can start using Pints for optimisation or sampling.

Citing Pints

If you use PINTS in any scientific work, please credit our work with a citation.

Examples and documentation

Pints comes with a number of detailed examples, hosted here on github. In addition, there is a full API documentation, hosted on

Installing Pints

You'll need the following requirements:

  • Python 2.7 or Python 3.4+
  • Python libraries: cma numpy matplotlib scipy

These can easily be installed using pip. To do this, first make sure you have the latest version of pip installed:

$ pip install --upgrade pip

Then navigate to the path where you downloaded Pints to, and install both Pints and its dependencies by typing:

$ pip install .

Or, if you want to install Pints as a developer, use

$ pip install -e .[dev,docs]

To uninstall again, type

$ pip uninstall pints

Contributing to Pints

If you'd like to help us develop Pints by adding new methods, writing documentation, or fixing embarassing bugs, please have a look at these guidelines first.


Pints is fully open source. For more information about its license, see LICENSE.

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