racecar 0.0.8

A lightweight Bayesian sampling package

A Python package collecting awesome MCMC sampling algorithms.
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Table of Contents

• Features
• Examples
• Installation
• License
• Contact

Features

• Lightweight Python library for sampling distributions in high dimensions using cutting-edge algorithms.
• Easily to use and simple to extend with new methods and use cases.

Examples

Quickstart example

We will sample points from the one-dimensional distribution using the random walk Metropolis algorithm, and then plot the results.

# Import racecar and numpy
import racecar as rc
import numpy as np

# Define the log likelihood function
def llh(x):
  return {
  'llh' : -( np.cos(2*x) + x**2/12 )
  }

# Create the sampler object and use Random Walk Metropolis
initial_condition = [0]
learning_rate = 0.5
S = rc.sampler(initial_condition, learning_rate, llh, algo="RWMetropolis")

# Sample some points, outputting arrays of position and log likelihood
Pos_traj, LLH_traj = S.sample(100000, output=['pos','llh'])

# Plot the results using matplotlib

More examples

Some more detailed examples are given in detailed Jupyter notebooks below

• Example 1
• Example 2
• Example 3

Installation

You can install the package from source by cloning this repo and using setup.py, the only dependencies are on numpy and scipy. Otherwise it is available on pip via

pip install racecar

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Made by Charles Matthews - www.cmatthe.ws - [e-mail]

Project Link: https://github.com/c-matthews/racecar