MCMC System

An MCMC System is a stochastic approximate Bayesian inference system that implements an MCMC algorithm to solve an MCMC task.

• Example(s):
  • PyMC3 System.
  • Stan System.
  • …
• Counter-Example(s):
  • a Variational Bayes Inference System.
• See: PyMC, Bayesian Inference System.

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References

2018

• https://github.com/pymc-devs/pymc
  • QUOTE: PyMC is a python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its flexibility and extensibility make it applicable to a large suite of problems. Along with core sampling functionality, PyMC includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics.
  • Features
    • PyMC provides functionalities to make Bayesian analysis as painless as possible. Here is a short list of some of its features:
    • Fits Bayesian statistical models with Markov chain Monte Carlo and other algorithms.
    • Includes a large suite of well-documented statistical distributions.
    • Uses NumPy for numerics wherever possible.
    • Includes a module for modeling Gaussian processes.
    • Sampling loops can be paused and tuned manually, or saved and restarted later.
    • Creates summaries including tables and plots.
    • Traces can be saved to the disk as plain text, Python pickles, SQLite or MySQL database, or hdf5 archives.
    • Several convergence diagnostics are available.
    • Extensible: easily incorporates custom step methods and unusual probability distributions.
    • MCMC loops can be embedded in larger programs, and results can be analyzed with the full power of Python.

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