PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI) algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.
- Intuitive model specification syntax, for example,
x ~ N(0,1)translates to
x = Normal('x',0,1)
- Powerful sampling algorithms, such as the No U-Turn Sampler, allow complex models with thousands of parameters with little specialized knowledge of fitting algorithms.
- Variational inference: ADVI for fast approximate posterior estimation as well as mini-batch ADVI for large data sets.
- Relies on Aesara which provides:
- Computation optimization and dynamic C or JAX compilation
- Numpy broadcasting and advanced indexing
- Linear algebra operators
- Simple extensibility
- Transparent support for missing value imputation