Bayesian Conjugate Models in Python
Project description
Conjugate Models
Bayesian conjugate models in Python
Installation
pip install conjugate-models
Features
- Connection to Scipy Distributions with
dist
attribute - Built in Plotting with
plot_pdf
andplot_pmf
methods - Vectorized Operations for parameters and data
- Indexing Parameters for subsetting and slicing
- Generalized Numerical Inputs for inputs other than builtins and numpy arrays
- Unsupported Distributions for sampling from unsupported distributions
Supported Models
Many likelihoods are supported including
Bernoulli
/Binomial
Categorical
/Multinomial
Poisson
Normal
(including linear regression)- and many more
Basic Usage
- Define prior distribution from
distributions
module - Pass data and prior into model from
models
modules - Analytics with posterior and posterior predictive distributions
from conjugate.distributions import Beta, BetaBinomial
from conjugate.models import binomial_beta, binomial_beta_posterior_predictive
# Observed Data
X = 4
N = 10
# Analytics
prior = Beta(1, 1)
prior_predictive: BetaBinomial = binomial_beta_posterior_predictive(n=N, beta=prior)
posterior: Beta = binomial_beta(n=N, x=X, beta_prior=prior)
posterior_predictive: BetaBinomial = binomial_beta_posterior_predictive(n=N, beta=posterior)
From here, do any analysis you'd like!
# Figure
import matplotlib.pyplot as plt
fig, axes = plt.subplots(ncols=2)
ax = axes[0]
ax = posterior.plot_pdf(ax=ax, label="posterior")
prior.plot_pdf(ax=ax, label="prior")
ax.axvline(x=X/N, color="black", ymax=0.05, label="MLE")
ax.set_title("Success Rate")
ax.legend()
ax = axes[1]
posterior_predictive.plot_pmf(ax=ax, label="posterior predictive")
prior_predictive.plot_pmf(ax=ax, label="prior predictive")
ax.axvline(x=X, color="black", ymax=0.05, label="Sample")
ax.set_title("Number of Successes")
ax.legend()
plt.show()
More examples on in the documentation.
Contributing
If you are interested in contributing, check out the contributing guidelines
Project details
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