**UPDATE 2015-02-16:** I’ve added a conceptual explanation of this code here.

Here’s a quick script that you can use (e.g., with a bash “alias” file, which sets shortcuts for the Unix-style command line) for making decisions with Bayes. It’s based off of Nate Silver’s version of Bayes’ Theorem. It can be run with `python /path/to/this/script.py`

.

#!/bin/python

# The above line just tells the interpreter that this is a python script.

```
```# Jacob Levernier, 2013

# This code is released CC0 (https://creativecommons.org/publicdomain/zero/1.0/) (Public Domain)

# THIS APPLIES BAYES' THEOREM (AS PRINTED ON P. 247 FF. OF NATE SILVER'S BOOK /THE SIGNAL AND THE NOISE/):

print "BAYES THEOREM:"

# (See https://en.wikibooks.org/wiki/Python_Programming/Input_and_output and http://docs.python.org/2/library/functions.html#input)

x=float(input('Make an estimation: How likely (out of 1.00) would you have estimated it would be for your hypothesis to be correct, before seeing this new information (The "prior")? '))

y=float(input('How likely (out of 1.00) do you think it is to see these results if your hypothesized explanation is correct? '))

z=float(input('How likely (out of 1.00) do you think it is to see these results if your hypothesized explanation is NOT correct? '))

posteriorProbability=(x*y)/((x*y)+(z*(1-x)))

`print "The revised estimate of how likely (out of 1.00) it is to see these results if your hypothesis is correct, given current circumstances, is",posteriorProbability`