Create a 2-hierarchy estimate of (normal) mean and standard deviation

Question

I have a normally distributed variable x (like product demand), an index id_1 (like product number) and a second index id_2 (like product group). My goal is to estimate the mean and the standard deviations for x hierarchically (all > product group > product). That's my data:

import numpy as np
import pymc3 as pm
import arviz as az

# data
my_step_1 = 0.4
my_step_2 = 4.1
sd_step_1 = 0.1
sd_step_2 = 0.2
my = 10
sd = .1
grp_n = 8
grps_1 = 5
grps_2 = 4

x = np.round(np.concatenate([np.random.normal(my + i * my_step_1 + j * my_step_2, \
                      sd + i * sd_step_1 + j * sd_step_2, grp_n) \
    for i in range(grps_1) for j in range(grps_2)]), 1) # demand
id_1 = np.repeat(np.arange(grps_1 * grps_2), grp_n) # group, product number
id_2 = np.tile(np.repeat(np.arange(grps_2), grp_n), grps_1) # super-group, product group
shape_1 = len(np.unique(id_1))
shape_2 = len(np.unique(id_2))

I've managed a single hierarchy:

with pm.Model() as model_h1:
    #
    mu_mu_hyper = pm.Normal('mu_mu_hyper', mu = 0, sd = 10)
    mu_sd_hyper = pm.HalfNormal('mu_sd_hyper', 10)
    sd_hyper = pm.HalfNormal('sd_hyper', 10)
    #
    mu = pm.Normal('mu', mu = mu_mu_hyper, sd = mu_sd_hyper, shape = shape_1)
    sd = pm.HalfNormal('sd', sd = sd_hyper, shape = shape_1)

    y = pm.Normal('y', mu = mu[id_1], sd = sd[id_1], observed = x)

    trace_h1 = pm.sample(1000)

#az.plot_forest(trace_h1, var_names=['mu', 'sd'], combined = True)

But how can I code 2 hierarchies?

# 2 hierarchies .. doesn't work
with pm.Model() as model_h2:  
    #
    mu_mu_hyper2 = pm.Normal('mu_mu_hyper2', mu = 0, sd = 10)
    mu_sd_hyper2 = pm.HalfNormal('mu_sd_hyper2', sd = 10)
    sd_mu_sd_hyper2 = pm.HalfNormal('sd_mu_sd_hyper2', sd = 10)
    sd_hyper2 = pm.HalfNormal('sd_hyper2', sd = 10)    
    #
    mu_mu_hyper1 = pm.Normal('mu_hyper1', mu = mu_mu_hyper2, sd = mu_sd_hyper2, shape = shape_2)
    mu_sd_hyper1 = pm.HalfNormal('mu_sd_hyper1', sd = sd_mu_sd_hyper2, shape = shape_2)
    sd_hyper1 = pm.HalfNormal('sd_hyper1', sd = sd_hyper2, shape = shape_2)
    #sd_hyper1 = pm.HalfNormal('sd_hyper1', sd = sd_hyper2[id_2], shape = shape_2)??
    #
    mu = pm.Normal('mu', mu = mu_mu_hyper1, sd = mu_sd_hyper1, shape = shape_1)
    sd = pm.HalfNormal('sd', sd = sd_hyper1, shape = shape_1)
    
   y = pm.Normal('y', mu = mu[id_1], sd = sd[id_1], observed = x)

    trace_h2 = pm.sample(1000)

Any restrictions as far as the libraries are concerned?

J. M. Arnold
– J. M. Arnold

2021-05-22 12:36:59 +00:00
Commented May 22, 2021 at 12:36 — J. M. Arnold
– J. M. Arnold, Commented May 22, 2021 at 12:36
no, even if I have to migrate, it gets me closer

r.user.05apr
– r.user.05apr

2021-05-22 13:57:44 +00:00
Commented May 22, 2021 at 13:57 — r.user.05apr
– r.user.05apr, Commented May 22, 2021 at 13:57

Sarath Gadde · Accepted Answer · 2021-08-06 07:40:08Z

You could try looping through product groups and use the mean, std for a group as constraints for the products belonging to this particular group.

# sample product group to product mapping
group_product_mapping = {0: [1, 2, 3], 1: [4, 5, 6]}
total_groups = len(group_product_mapping.keys())
with pm.model() as model_h:
    mu_all = pm.Normal('mu_all', 0, 10)
    sd_all = pm.HalfNormal('sd_all', 10)
    sd_mu_group = pm.HalfNormal('sd_mu_group', 10)
    
    # group parameters constrained to mu, sd from all
    mu_group = pm.Normal('mu_group', mu_all, sd_all, shape=total_groups) 
    sd_group = pm.HalfNormal('sd_group', sd_mu_group, shape=total_groups)
    
    mu_products = dict()
    # iterate through groups and constrain product parameters to the product group they belong to
    for idx, group in enumerate(group_product_mapping.keys()):
        mu_products[group] = pm.Normal(f'mu_products_{group}', mu_group[idx], sd_group[idx], shape=len(group_product_mapping[group]))
        sd_produtcs[group] = pm.HalfNormal(f'sd_mu_products_{group}', 10)

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