From charlesreid1

(Created page with "Python package for performing Monte Carlo simulations. PyMC3 is the newest and preferred version of the software. =Quick Start= ==Importing Components== The quick start gu...")
 
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=Resources=
=Resources=


PyMC3 on Github: https://github.com/pymc-devs/pymc3
Github: PyMC3 repository
* https://github.com/pymc-devs/pymc3


Bayesian Methods for Hackers: https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-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=
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Revision as of 23:42, 5 November 2017

Python package for performing Monte Carlo simulations.

PyMC3 is the newest and preferred version of the software.

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')


Resources

Github: PyMC3 repository

Book: Bayesian Methods for Hackers by Cam Davidson

Book: Doing Bayesian Data Analysis by John Kruschke

iPython Notebook: Doing Bayesian Data Analysis

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