PyMC
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1. Introduction
2. Installation
3. Tutorial
4. Building models
5. Fitting Models
6. Saving and managing sampling results
7. Model checking and diagnostics
8. Extending PyMC
9. Probability distributions
10. Conclusion
11. Acknowledgements
12. Appendix: Markov Chain Monte Carlo
13. List of References
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PyMC User’s Guide
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PyMC User’s Guide
¶
Contents:
1. Introduction
1.1. Purpose
1.2. Features
1.3. What’s new in version 2
1.4. Usage
1.5. History
1.6. Relationship to other packages
1.7. Getting started
2. Installation
2.1. Dependencies
2.2. Installation using EasyInstall
2.3. Installing from pre-built binaries
2.4. Compiling the source code
2.5. Installing from GitHub
2.6. Running the test suite
2.7. Bugs and feature requests
3. Tutorial
3.1. An example statistical model
3.2. Two types of variables
3.3. Parents and children
3.4. Variables’ values and log-probabilities
3.5. Fitting the model with MCMC
3.6. Fine-tuning the MCMC algorithm
3.7. Beyond the basics
4. Building models
4.1. The Stochastic class
4.2. Data
4.3. The Deterministic class
4.4. Containers
4.5. The Potential class
4.6. Graphing models
4.7. Class LazyFunction and caching
5. Fitting Models
5.1. Creating models
5.2. The Model class
5.3. Maximum a posteriori estimates
5.4. Normal approximations
5.5. Markov chain Monte Carlo: the MCMC class
5.6. The Sampler class
5.7. Step methods
5.8. Gibbs step methods
6. Saving and managing sampling results
6.1. Accessing Sampled Data
6.2. Saving Data to Disk
6.3. Reloading a Database
6.4. Writing a New Backend
7. Model checking and diagnostics
7.1. Convergence Diagnostics
7.2. Autocorrelation Plots
7.3. Goodness of Fit
8. Extending PyMC
8.1. Nonstandard Stochastics
8.2. User-defined step methods
8.3. New fitting algorithms
8.4. A second warning: Don’t update stochastic variables’ values in-place
9. Probability distributions
9.1. Discrete distributions
9.2. Continuous distributions
9.3. Multivariate discrete distributions
9.4. Multivariate continuous distributions
10. Conclusion
11. Acknowledgements
12. Appendix: Markov Chain Monte Carlo
12.1. Monte Carlo Methods in Bayesian Analysis
12.2. Markov Chains
12.3. Why MCMC Works: Reversible Markov Chains
12.4. Gibbs Sampling
12.5. The Metropolis-Hastings Algorithm
13. List of References
Indices and tables
¶
Index
Module Index
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