Probabilistic programming in Python using PyMC3
John Salvatier, Thomas V. Wiecki low, Christopher Fonnesbeck
Probabilistic programming allows for automatic Bayesian inference on user-defined probabilistic models. Recent advances in Markov chain Monte Carlo (MCMC) sampling allow inference on increasingly complex models. This class of MCMC, known as Hamiltonian Monte Carlo, requires gradient information which is often not readily available. PyMC3 is a new open source probabilistic programming framework written in Python that uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs on-the-fly to C for increased speed. Contrary to other probabilistic programming languages, PyMC3 allows model specification directly in Python code. The lack of a domain specific language allows for great flexibility and direct interaction with the model. This paper is a tutorial-style introduction to this software package.
What this paper cites, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Advances in Neural Information Processing Systems 28 | 2015 | 14,672 |
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Topics
| Gaussian Processes and Bayesian Inference | Computer Science |
| Bayesian Modeling and Causal Inference | Computer Science |
| Computational Physics and Python Applications | Computer Science |
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