PyMC: Difference between revisions
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PyMC3 is the newest and preferred version of the software. | PyMC3 is the newest and preferred version of the software. | ||
=Installing= | |||
==Pip== | |||
PyMC3 can be installed with pip: | |||
<pre> | |||
pip3 install pymc3 | |||
</pre> | |||
The prerequisites are: | |||
* [[Theano]] | |||
* [[Numpy]] | |||
* [[Scipy]] | |||
* [[Pandas]] | |||
* [[Matplotlib]] | |||
Optional prerequisites: | |||
* GPflow | |||
* Patsy | |||
* scikit-learn (specifically, scikits.sparse) | |||
=Quick Start= | =Quick Start= | ||
| Line 22: | Line 44: | ||
</pre> | </pre> | ||
=PyMC3 and Keras= | |||
To have PyMC3 and Keras work together for a convolutional autoencoder: http://docs.pymc.io/notebooks/convolutional_vae_keras_advi.html | |||
This utilizes the Theano backend for Keras | |||
=Resources= | =Resources= | ||
Github: PyMC3 repository | |||
* https://github.com/pymc-devs/pymc3 | |||
* Official documentation: http://docs.pymc.io/ | |||
Bayesian Methods for Hackers | Book: Bayesian Methods for Hackers by Cam Davidson | ||
* https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | |||
* Illustrates how to do Bayesian statistics using PyMC (the brute-force computational approach, rather than the math-heavy approach) | * Illustrates how to do Bayesian statistics using PyMC (the brute-force computational approach, rather than the math-heavy approach) | ||
Book: Doing Bayesian Data Analysis by John Kruschke | |||
* https://github.com/aloctavodia/Doing_bayesian_data_analysis | |||
* doingbayesiandataanalysis.blogspot.com.ar | |||
* Originally written for BUGS and R, ported to PyMC3 | |||
iPython Notebook: Doing Bayesian Data Analysis | |||
* doingbayesiandataanalysis.blogspot.com.ar | |||
* author: https://github.com/hgbrian | |||
=Flags= | =Flags= | ||
Latest revision as of 11:05, 6 November 2017
Python package for performing Monte Carlo simulations.
PyMC3 is the newest and preferred version of the software.
Installing
Pip
PyMC3 can be installed with pip:
pip3 install pymc3
The prerequisites are:
Optional prerequisites:
- GPflow
- Patsy
- scikit-learn (specifically, scikits.sparse)
Quick Start
Importing Components
The quick start guide is here: http://docs.pymc.io/notebooks/api_quickstart.html
It starts by importing the necessary components:
%matplotlib inline
import numpy as np
import theano.tensor as tt
import pymc3 as pm
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_context('notebook')
PyMC3 and Keras
To have PyMC3 and Keras work together for a convolutional autoencoder: http://docs.pymc.io/notebooks/convolutional_vae_keras_advi.html
This utilizes the Theano backend for Keras
Resources
Github: PyMC3 repository
- https://github.com/pymc-devs/pymc3
- Official documentation: http://docs.pymc.io/
Book: Bayesian Methods for Hackers by Cam Davidson
- https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
- Illustrates how to do Bayesian statistics using PyMC (the brute-force computational approach, rather than the math-heavy approach)
Book: Doing Bayesian Data Analysis by John Kruschke
- https://github.com/aloctavodia/Doing_bayesian_data_analysis
- doingbayesiandataanalysis.blogspot.com.ar
- Originally written for BUGS and R, ported to PyMC3
iPython Notebook: Doing Bayesian Data Analysis
- doingbayesiandataanalysis.blogspot.com.ar
- author: https://github.com/hgbrian