Bayesian Update

Easy to use Bayesian inference incrementally by setting the posterior to the prior for the next set of data points.

Abstractly, something like this:

prior = ...

for batch_size in batch_sizes:
    data = sample(n=batch_size)

    posterior = model(data, prior=prior)

    prior = posterior

Below are some concrete examples

Normal Distribution

We can use the normal model with NormalInverseGamma distribution as prior

import numpy as np
import matplotlib.pyplot as plt

from conjugate.distributions import NormalInverseGamma
from conjugate.models import normal_normal_inverse_gamma

def create_sampler(mu, sigma, rng): 
    """Generate a sampler from a normal distribution with mean `mu` and standard deviation `sigma`."""
    def sample(n: int): 
        return rng.normal(loc=mu, scale=sigma, size=n)

    return sample


mu = 5.0
sigma = 2.5
rng = np.random.default_rng(0)
sample = create_sampler(mu=mu, sigma=sigma, rng=rng)


prior = NormalInverseGamma(
    mu=0, 
    alpha=1, beta=1, 
    nu=1, 
)

cumsum = 0
batch_sizes = [5, 10, 25]
ax = plt.gca()
for batch_size in batch_sizes:
    data = sample(n=batch_size)

    posterior = normal_normal_inverse_gamma(
        x_total=data.sum(), 
        x2_total=(data ** 2).sum(), 
        n=batch_size, 
        normal_inverse_gamma_prior=prior
    )

    beta_samples, variance_samples = posterior.sample_beta(size=1000, return_variance=True, random_state=rng)

    cumsum += batch_size
    label = f"n={cumsum}"
    ax.scatter(variance_samples ** 0.5, beta_samples, alpha=0.25, label=label)

    prior = posterior 

ax.scatter(sigma, mu, color="black", label="true")
ax.set(
    xlabel="$\sigma$", 
    ylabel="$\mu$", 
    xlim=(0, None), 
    ylim=(0, None), 
    title="Updated posterior samples of $\mu$ and $\sigma$"
)
ax.legend()

plt.show()

Binomial Distribution

With a Binomial model, we can assume as Beta prior

import numpy as np

import matplotlib.pyplot as plt

from conjugate.distributions import Beta
from conjugate.models import binomial_beta

def create_sampler(p, rng): 
    def sampler(n: int): 
        return rng.binomial(n=n, p=p)

    return sampler

prior = Beta(1, 1)

p = 0.75
rng = np.random.default_rng(0)
sample = create_sampler(p=p, rng=rng)

ax = plt.gca()
cumsum = 0
batch_sizes = [5, 25, 50]
for batch_size in batch_sizes: 
    x = sample(n=batch_size)

    posterior = binomial_beta(
        x=x, 
        n=batch_size, 
        beta_prior=prior, 
    )

    cumsum += batch_size
    label = f"n={cumsum}"

    posterior.plot_pdf(ax=ax, label=label)

    prior = posterior

ax.axvline(p, label="true p", color="black", ymax=0.05)
ax.set(
    xlabel="p", 
    ylim=(None, 10), 
)
ax.legend()
plt.show()

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