PyMC: Bayesian Stochastic modelling in Python

Journal of Statistical Software

Volumn 35, Issue 4, 2010, Pages 1-81

  Patil, Anand ^a   Huard, David ^b   Fonnesbeck, Christopher J ^c  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b MCGILL UNIVERSITY   (Canada)

^c VANDERBILT UNIVERSITY   (United States)

Author keywords

Bayesian modeling; Markov chain monte carlo; Python; Simulation

Indexed keywords

EID: 77954196403     PISSN: 15487660     EISSN: 15487660     Source Type: Journal    
DOI: 10.18637/jss.v035.i04     Document Type: Article

References (53)

1. Akaike H (1973). Information Theory as an Extension of the Maximum Likelihood Principle. In BN Petrov, F Csaki (eds.), Second International Symposium on Information Theory, pp. 267-281. Akademiai Kiado, Budapest.
2. Alted F, Vilata I, Prater S, Mas V, Hedley T, Valentino A, Whitaker J (2010). Py Tables User's Guide: Hierarchical Datasets in Python-Release 2.2. URL http://www.pytables. org/.
3. Azzalini A (2010). The Skew-Normal Probability Distribution (and Related Distributions, such as the Skew-t). URL http://azzalini.stat.unipd.it/SN/.
4. Bernardo JM, Berger J, Dawid AP, Smith JFM (eds.) (1992). Bayesian Statistics 4. Oxford University Press, Oxford.
5. Brooks SP, Catchpole EA, Morgan BJT (2000). Bayesian Animal Survival Estimation. Statistical Science, 15, 357-376.
6. Burnham KP, Anderson DR (2002). Model Selection and Multi-Model Inference: A Practical, Information-Theoretic Approach. Springer-Verlag, New York.
7. Carrera E, Theune C (2010). Python Interface to Graphviz's Dot Language. URL http://code.google.com/p/pydot/.
8. Christakos G (2002). On the Assimilation of Uncertain Physical Knowledge Bases: Bayesian and Non-Bayesian Techniques. Advances in Water Resources, 25(8-12), 1257-1274.
9. Daumé III H (2007). HBC: Hierarchical Bayes Compiler. URL http://hal3.name/HBC
10. Dustman JA (2010). MySQL for Python. URL http://sourceforge.net/projects/mysqlpython
11. Eby PJ (2010). setuptools 0.6c11. URL http://pypi.python.org/pypi/setuptools
12. Enthought, Inc (2010).Enthought Python Distribution.URL http://www.enthought.com/.
13. Ewing G (2010). Pyrex-A Language for Writing Python Extension Modules. URL http://www.cosc.canterbury.ac.nz/greg.ewing/python/Pyrex/.
14. Free Software Foundation, Inc (2010). gcc, the GNU Compiler Collection. URL http://gcc.gnu.org
15. Gamerman D (1997). Markov Chain Monte Carlo: Statistical Simulation for Bayesian Inference. Chapman and Hall, London
16. Gansner ER, North SC (1999). An Open Graph Visualization System and Its Applications to Software Engineering. Software-Practice and Experience, 00(S1), 1-5.
17. Gelman A, Carlin JB, Stern HS, Rubin DB (2004). Bayesian Data Analysis. 2nd edition. Chapman and Hall/CRC, Boca Raton, FL.
18. Gelman A, Meng XL, Stern H (1996). Posterior Predictive Assessment of Model Fitness via Realized Discrepencies. Statistica Sinica, 6, 733-807.
19. Geweke J (1992). Evaluating the Accuracy of Sampling-Based Approaches to Calculating Posterior Moments, pp. 169-193. In Bernardo, Berger, Dawid, and Smith (1992).
20. Haario H, Saksman E, Tamminen J (2001). An Adaptive Metropolis Algorithm. Bernoulli, 7(2),223-242.
21. Hadfield JD (2010). MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package. Journal of Statistical Software, 33(2), 1-22. URL http: //www.jstatsoft.org/v33/i02
22. Hunter JD (2007). matplotlib: A 2D Graphics Environment. Computing in Science & Engineering, 9(3), 90-95. doi:10.1109/MCSE.2007.55.
23. Jarrett RG (1979). A Note on the Intervals Between Coal Mining Disasters. Biometrika, 66,191-193.
24. Jaynes ET (2003). Probability Theory: The Logic of Science. Cambridge University Press, Cambridge.
25. Jones E, Oliphant T, Peterson P (2001). SciPy: Open Source Scientific Tools for Python. URL http://www.scipy.org/.
26. Jordan MI (2004). Graphical Models. Statistical Science, 19(1), 140-155.
27. Kerman J, Gelman A (2004). Fully Bayesian Computing. URL http://stat.columbia.edu/ ~gelman/research/unpublished/fullybayesiancomputing-nonblinded.pdf.
28. Langtangen HP (2009). Python Scripting for Computational Science. Springer-Verlag, Hei-delberg.
29. Lauritzen SL, Dawid AP, Larsen BN, Leimer HG (1990).Independence Properties of Directed Markov Fields. Networks, 20, 491-505.
30. Lunn D, Thomas A, Best NG, Spiegelhalter DJ (2000). WinBUGS-A Bayesian Modelling Framework: Concepts, Structure, and Extensibility. Statistics and Computing, 10, 325-337.
31. Lutz M (2007). Learning Python. O'Reilly Media, Inc., Sebastopol.
32. Martin AD, Quinn KM, Park JH (2009). MCMCpack: Markov Chain Monte Carlo (MCMC) Package. R package version 1.0-4, URL http://CRAN.R-project.org/package=MCMCpack.
33. Oberhumer MFXJ (2008). LZO Real-Time Data Compression Library. URL http://www.oberhumer.com/opensource/lzo/.
34. Oliphant TE (2006). Guide to NumPy. Provo, UT. URL http://www.tramy.us/.
35. Oracle Corporation (2010). MySQL: The World's Most Popular Open Source Database. URL http://www.mysql.com/.
36. Pellerin J (2010). nose Is Nicer Testing for Python. URL http://somethingaboutorange.com/mrl/projects/nose/.
37. Pérez F, Granger BE (2007). IPython: a System for Interactive Scientific Computing. Computing in Science and Engineering, 9(3), 21-29.
38. Peters C (2010). MinGW: Minimalist GNU for Windows. URL http://www.mingw.org/.
39. Plummer M (2003). JAGS: A Program for Analysis of Bayesian Graphical Models Using Gibbs Sampling. In K Hornik, F Leisch, A Zeileis (eds.), Proceedings of the 3rd International Workshop on Distributed Statistical Computing, Vienna, Austria. ISSN 1609-395X, URL http://www.ci.tuwien.ac.at/Conferences/DSC-2003/Proceedings/.
40. Plummer M, Best N, Cowles K, Vines K (2008). coda: Output Analysis and Diagnostics for MCMC. R package version 0.13-3, URL http://CRAN.R-project.org/package=coda.
41. Python Software Foundation (2005). MacPython. URL http://homepages.cwi.nl/~jack/macpython/.
42. Raftery AE, Lewis SM (1995a). The Number of Iterations, Convergence Diagnostics and Generic Metropolis Algorithms. In DJS W R Gilks, S Richardson (eds.), Practical Markov Chain Monte Carlo. Chapman and Hall, London.
43. Raftery AE, Lewis SM (1995). Gibbsit Version 2.0. URL http://lib.stat.cmu.edu/general/gibbsit/.
44. R Development Core Team (2010). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/.
45. Roberts GO, Rosenthal JS (2007). Implementing Componentwise Hastings Algorithms. Journal of Applied Probability, 44(2), 458-475.
46. Roelofs G, loup Gailly J, Adler M (2010). zlib: A Massively Spiffy Yet Delicately Unobtrusive Compression Library. URL http://www.zlib.net/.
47. Schwarz G (1978). Estimating the Dimension of a Model. The Annals of Statistics, 6(2), 461-464.
48. Seward J (2007). bzip2 and libbzip2, Version 1.0.5-A Program and Library for Data Compression. URL http://www.bzip.org/.
49. Spiegelhalter DJ, Thomas A, Best NG, Lunn D (2003). WinBUGS Version 1.4 User Manual. MRC Biostatistics Unit, Cambridge. URL http://www.mrc-bsu.cam.ac.uk/bugs/.
50. The HDF Group (2010). HDF5. URL http://www.hdfgroup.org/HDF5/.
51. The SQLite Development Team (2010). SQLite. URL http://www.sqlite.org/.
52. Torvalds L (2010). git: The Fast Version Control System. URL http://git-scm.com/.
53. van Rossum G (2010). The Python Library Reference Release 2.6.5. URL http://docs.python.org/library/.