Hierarchical model with mixture prior

Hi all,

I’m trying to fit a hierarchical regression model where the slopes follow a bimodal distribution (a mixture of two normals) rather than being normally distributed around a group mean. Essentially each beta value should come from one of two normal distributions, but I have no prior knowledge of which distribution it comes from.

I’ve tried implementing this using a NormalMixture as the group beta and estimating the mean of each of its two component distributions. However the component distributions tend to just collapse to the overall mean rather than giving the true bimodal distribution.

This may be a dumb way to approach this, or simply impossible, but I’d appreciate any help!

Here is a minimal example:

import pandas as pd
import pymc3 as pm

df = pd.read_csv('example_data.csv')

with pm.Model() as hierarchical_model:

    # Means of component distributions
    mus = pm.Normal('mixture_mus', mu=0.5, sd=0.5, shape=2)
    # Group-level beta
    beta_mu = pm.NormalMixture('beta_mu', [0.5, 0.5], mu=mus, sd=0.5)
    # Betas
    beta = pm.Normal("beta", mu=beta_mu, sd=1, shape=len(df['level2'].unique()))
    # Error
    eps = pm.HalfNormal('eps', 1)
    # Estimated y values
    y_est = beta[df['level2'].values] * df['x']
    # Likelihood
    y_like = pm.Normal('likelihood', y_est, sd=eps, observed=df['y'])

with hierarchical_model:
    trace = pm.sample(3000, chains=1, tune=500)

And for reference, the true beta values used to simulate data (which I’d expect the group beta to look like):

example_data.csv (633.6 KB)


I was facing what seems to be a similar problem some days ago when trying to estimate the parameters of two linear laws mixed in the same dataset.
I managed to get some good results by using a NormalMixture as likelihood. My understanding is that a simple Normal law will not be able to capture the bimodality of the data distribution as it is only a single mode.
For parameterization, I have followed the first one given in this post.

I hope this can help.

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