BAyesian Model-Building Interface (Bambi) in Python#
Bambi is a high-level Bayesian model-building interface written in Python. It works with two probabilistic programming frameworks, PyMC3 or PyStan, and is designed to make it extremely easy to fit Bayesian mixed-effects models common in biology, social sciences and other disciplines.
New Features#
Bambi version 0.1.1
will be the final version supporting Python 2, but look forward to the forthcoming Bambi version 0.1.2
!
Dependencies#
Bambi is tested on Python 3.6 and depends on NumPy, Pandas, PyMC3, PyStan, Patsy and ArviZ (see requirements.txt for version information).
Installation#
The latest release of Bambi can be installed using pip:
pip install bambi
Alternatively, if you want the bleeding edge version of the package, you can install from GitHub:
pip install git+https://github.com/bambinos/bambi.git
Usage#
A simple fixed effects model is shown below as example.
from bambi import Model
import pandas as pd
# Read in a tab-delimited file containing our data
data = pd.read_table('my_data.txt', sep='\t')
# Initialize the model
model = Model(data)
# Fixed effects only model
results = model.fit('DV ~ IV1 + IV2', samples=1000, chains=4)
# Use ArviZ to plot the results
az.plot_trace(results)
# Key summary and diagnostic info on the model parameters
az.summary(results)
# Drop the first 100 samples (burn-in)
results_bi = results.sel(draw=slice(100, None))
For a more in-depth introduction to Bambi see our Quickstart or our set of example notebooks.
Contributing#
We welcome contributions from interested individuals or groups! For information about contributing to Bambi, check out our instructions, policies, and guidelines here.
Contributors#
See the GitHub contributor page.
Contents#
- Getting Started
- Examples
- API Reference