Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available.

The Core Equation

Where:

• P(A) is the Prior probability.
• P(B|A) is the Likelihood.
• P(A|B) is the Posterior probability.

Code Implementation

import numpy as np

# 1. Define Prior
prior = np.ones(100) / 100 

def update(prior, heads, total):
    likelihood = p_grid**heads * (1 - p_grid)**(total - heads)
    posterior = likelihood * prior
    return posterior / posterior.sum()