Mixture Models

Mixture Models

Table of Contents

• Finite Mixture Models
• Label Switching Problem
• Marginalized Mixtures
• Diagnostics for Mixtures

Finite Mixture Models

Mixture models assume data comes from multiple subpopulations, each described by its own distribution. Use when:

• Data shows multimodality
• Subgroups exist but group membership is unknown
• You need to cluster observations probabilistically

Gaussian Mixture

For simple univariate Gaussian mixtures, use pm.NormalMixture:

import pymc as pm
import numpy as np

coords = {"component": range(K)}

with pm.Model(coords=coords) as gmm:
    # Mixture weights (Dirichlet prior)
    w = pm.Dirichlet("w", a=np.ones(K), dims="component")

    # Component means (with ordering constraint to avoid label switching)
    mu = pm.Normal("mu", mu=0, sigma=10, dims="component",
                   transform=pm.distributions.transforms.ordered)

    # Component standard deviations
    sigma = pm.HalfNormal("sigma", sigma=2, dims="component")

    # Mixture likelihood
    y = pm.NormalMixture("y", w=w, mu=mu, sigma=sigma, observed=y_obs)

General Mixtures with pm.Mixture

For mixtures of arbitrary distributions, use pm.Mixture:

with pm.Model(coords=coords) as general_mixture:
    # Weights
    w = pm.Dirichlet("w", a=np.ones(K))

    # Define component distributions
    components = [
        pm.Normal.dist(mu=pm.Normal("mu_0", 0, 5), sigma=pm.HalfNormal("sigma_0", 2)),
        pm.StudentT.dist(nu=3, mu=pm.Normal("mu_1", 0, 5), sigma=pm.HalfNormal("sigma_1", 2)),
    ]

    # Mixture
    y = pm.Mixture("y", w=w, comp_dists=components, observed=y_obs)

Mixture of Regressions

When different subgroups follow different regression relationships:

with pm.Model(coords={"component": range(K), "obs": range(N)}) as mixture_regression:
    # Mixture weights
    w = pm.Dirichlet("w", a=np.ones(K))

    # Component-specific regression coefficients
    alpha = pm.Normal("alpha", mu=0, sigma=5, dims="component")
    beta = pm.Normal("beta", mu=0, sigma=2, dims="component")
    sigma = pm.HalfNormal("sigma", sigma=1, dims="component")

    # Component distributions (one regression per component)
    components = [
        pm.Normal.dist(mu=alpha[k] + beta[k] * x, sigma=sigma[k])
        for k in range(K)
    ]

    y = pm.Mixture("y", w=w, comp_dists=components, observed=y_obs, dims="obs")

Label Switching Problem

The Problem

In mixture models, the likelihood is invariant to permutations of component labels. If you swap "component 1" and "component 2", the joint probability is unchanged. This creates:

• Multimodal posterior: K! equivalent modes
• Meaningless component-wise summaries: Averaging across modes mixes components
• Failed diagnostics: R-hat appears bad because chains find different modes

Detecting Label Switching

# Trace plots show "switching" between modes
az.plot_trace(idata, var_names=["mu"])

# Pair plots show symmetric clusters
az.plot_pair(idata, var_names=["mu"], coords={"component": [0, 1]})

Solution 1: Ordering Constraints (Recommended)

Impose an ordering on component parameters to break symmetry:

import pytensor.tensor as pt

with pm.Model(coords=coords) as gmm_ordered:
    w = pm.Dirichlet("w", a=np.ones(K))

    # Unordered means on unconstrained space
    mu_raw = pm.Normal("mu_raw", mu=0, sigma=10, dims="component")

    # Apply ordering constraint: mu[0] < mu[1] < ... < mu[K-1]
    mu = pm.Deterministic("mu", pt.sort(mu_raw), dims="component")

    sigma = pm.HalfNormal("sigma", sigma=2, dims="component")
    y = pm.NormalMixture("y", w=w, mu=mu, sigma=sigma, observed=y_obs)

Or use PyMC's built-in ordered transform:

# This applies the ordered transform directly
mu = pm.Normal("mu", mu=0, sigma=10, dims="component",
               transform=pm.distributions.transforms.ordered)

Note: Ordering constraints only work when the ordered parameter differs meaningfully across components. For equal component means, use other identifiability strategies.

Solution 2: Post-Processing (Relabeling)

When ordering constraints aren't natural, relabel samples post-hoc:

# Simple relabeling based on component means
def relabel_samples(idata):
    """Relabel mixture components by sorting means within each draw."""
    mu = idata.posterior["mu"].values  # (chain, draw, component)

    # Get sort indices for each draw
    sort_idx = np.argsort(mu, axis=-1)

    # Apply to all component-indexed variables
    for var in ["mu", "sigma", "w"]:
        if var in idata.posterior:
            vals = idata.posterior[var].values
            # Gather along component axis using sort indices
            relabeled = np.take_along_axis(vals, sort_idx, axis=-1)
            idata.posterior[var].values = relabeled

    return idata

For more sophisticated relabeling, see the label.switching R package or implement the Stephens algorithm.

When Label Switching Doesn't Matter

If you only care about predictions (not component interpretation), label switching is harmless:

# Posterior predictive is invariant to label permutations
with model:
    pm.sample_posterior_predictive(idata, extend_inferencedata=True)

# This is unaffected by label switching
az.plot_ppc(idata)

Marginalized Mixtures

Why Marginalize

Standard mixture models sample discrete component assignments, which:

• Requires specialized samplers (not NUTS)
• Often mixes poorly
• Scales badly with data size

Marginalization integrates out the discrete assignments analytically, enabling efficient NUTS sampling.

Using pm.Mixture (Automatic Marginalization)

pm.Mixture and pm.NormalMixture automatically marginalize:

# This is already marginalized - no discrete latent variables
y = pm.NormalMixture("y", w=w, mu=mu, sigma=sigma, observed=y_obs)

pymc-extras MarginalMixture

For more complex marginalizations:

import pymc_extras as pmx

with pm.Model() as marginal_model:
    # Discrete latent variable (will be marginalized)
    z = pm.Categorical("z", p=w)  # Not sampled directly

    # Conditional distributions
    y = pmx.MarginalMixture(
        "y",
        dist=[
            pm.Normal.dist(mu[0], sigma[0]),
            pm.Normal.dist(mu[1], sigma[1]),
        ],
        support_idxs=z,
        observed=y_obs,
    )

When to Use Standard vs Marginalized

Diagnostics for Mixtures

Checking for Label Switching

# 1. Trace plots should NOT show "switching" patterns
az.plot_trace(idata, var_names=["mu", "w"])

# 2. Rank plots should be uniform (not bimodal)
az.plot_rank(idata, var_names=["mu"])

# 3. R-hat should be < 1.01 (won't be if label switching occurs)
summary = az.summary(idata, var_names=["mu", "sigma", "w"])
print(summary[["r_hat"]])

Posterior Predictive Checks

with model:
    pm.sample_posterior_predictive(idata, extend_inferencedata=True)

# Check if mixture captures data distribution shape
az.plot_ppc(idata, kind="kde")

# For multimodal data, cumulative is often clearer
az.plot_ppc(idata, kind="cumulative")

Model Selection for Number of Components

Compare models with different K using LOO-CV:

# Fit models with K=2, 3, 4 components
models = {}
for K in [2, 3, 4]:
    with build_mixture_model(K) as model:
        idata = pm.sample(nuts_sampler="nutpie")
        models[f"K={K}"] = idata

# Compare
comparison = az.compare(models, ic="loo")
print(comparison[["rank", "elpd_loo", "d_loo", "weight"]])
az.plot_compare(comparison)

Caution: LOO can be unreliable for mixture models due to high Pareto k values. Consider:

• K-fold cross-validation when LOO diagnostics fail
• WAIC as a secondary check
• Domain knowledge about plausible number of components

Assessing Component Separation

# Posterior distribution of component means
az.plot_posterior(idata, var_names=["mu"])

# Check overlap between components
# Well-separated components have non-overlapping HDIs
summary = az.summary(idata, var_names=["mu"], hdi_prob=0.94)
print(summary[["mean", "hdi_3%", "hdi_97%"]])

See Also

• priors.md - Prior selection for mixture components
• diagnostics.md - General convergence diagnostics
• gotchas.md - Common modeling pitfalls