pymc3

We have hosted the application pymc3 in order to run this application in our online workstations with Wine or directly.

Quick description about pymc3:

PyMC3 allows you to write down models using an intuitive syntax to describe a data generating process. Fit your model using gradient-based MCMC algorithms like NUTS, using ADVI for fast approximate inference � including minibatch-ADVI for scaling to large datasets, or using Gaussian processes to build Bayesian nonparametric models. PyMC3 includes a comprehensive set of pre-defined statistical distributions that can be used as model building blocks. Sometimes an unknown parameter or variable in a model is not a scalar value or a fixed-length vector, but a function. A Gaussian process (GP) can be used as a prior probability distribution whose support is over the space of continuous functions. PyMC3 provides rich support for defining and using GPs. Variational inference saves computational cost by turning a problem of integration into one of optimization. PyMC3's variational API supports a number of cutting edge algorithms, as well as minibatch for scaling to large datasets.

Features:

• Intuitive model specification syntax
• Powerful sampling algorithms
• Complex models with thousands of parameters with little specialized knowledge of fitting algorithms
• ADVI for fast approximate posterior estimation as well as mini-batch ADVI for large data sets
• Variational inference
• Computation optimization and dynamic C or JAX compilation

Programming Language: Python.
Categories: