PyMC: Bayesian Stochastic Modelling in Python. 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.
Keywords for this software
References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
- Wright, James R.; Leyton-Brown, Kevin: Predicting human behavior in unrepeated, simultaneous-move games (2017)
- Hernández-Lobato, José Miguel; Gelbart, Michael A.; Adams, Ryan P.; Hoffman, Matthew W.; Ghahramani, Zoubin: A general framework for constrained Bayesian optimization using information-based search (2016)
- Luttinen, Jaakko: BayesPy: variational Bayesian inference in Python (2016)
- Parish, Eric J.; Duraisamy, Karthik: A paradigm for data-driven predictive modeling using field inversion and machine learning (2016)
- Marcin Korzeń; Szymon Jaroszewicz: PaCAL: A Python Package for Arithmetic Computations with Random Variables (2014)
- Cohen, Michael I.; Cutler, Curt; Vallisneri, Michele: Searches for cosmic-string gravitational-wave bursts in Mock LISA Data (2010)