Unable to Initialize Large Correlation Matrix In PyStan

I’m running into an issue when I try to run the following Stan model (pystan 3.3.0, python 3.8.8) with a fairly large correlation matrix (J = 100); when I subset down my data such that the correlation matrix is smaller (J = 10), I’m able to run without issue.

The specific error I’m recovering is RuntimeError: Initialization failed.; I’ve tried initializing my tau and Omega parameters with values .005, 1, and zero, to no effect. I’ve additionally reparametrized my model in terms of cholesky_factor_corr /lkj_corr_cholesky for omega, and multi_normal_cholesky for beta, but continue to receive the same Initialization failed error message.

Appreciate any guidance folks might have here.

data {

  // Training Data (Individual-Level)
  /// Model Metadata
  int<lower=3> K; // Number of target classes
  int<lower=0> N; // Number of interviews
  int<lower=1> J; // Number of groups
  int<lower=0> L; // Number of group-level predictors

  //// Number of Levels for Varying Intercepts
  int<lower=0> N_a;
  int<lower=0> N_b;
  int<lower=0> N_c;
  int<lower=0> N_d;
  int<lower=0> N_e;
  int<lower=0> N_f;

  /// Interview Targets
  array[N] int<lower=1, upper=K> y; // Target

  /// Varying Intercept Levels
  array[N] int<lower=1,upper=N_a> a;
  array[N] int<lower=1,upper=N_b> b;
  array[N] int<lower=1,upper=N_c> c;
  array[N] int<lower=1,upper=N_d> d;
  array[N] int<lower=1,upper=N_e> e;
  array[N] int<lower=1,upper=N_f> f;

  //// Group-Level Predictors
  array[N] int<lower=1, upper=J> jj; // Group assignment for individual
  array[J] row_vector[L] u_jj;       // Groupl-Level design matrix

parameters {

  /// Prior Variances for Varying Intercepts
  real<lower=0> sigma_a;
  real<lower=0> sigma_b;
  real<lower=0> sigma_c;
  real<lower=0> sigma_d;
  real<lower=0> sigma_e;
  real<lower=0> sigma_f;
  real<lower=0> sigma_jj;

  /// Varying Intercepts
  vector[K] alpha_zero;
  vector<multiplier=sigma_a>[K] alpha_a;
  vector<multiplier=sigma_b>[K] alpha_b;
  array[N_c] vector<multiplier=sigma_c>[K] alpha_c;
  array[N_d] vector<multiplier=sigma_d>[K] alpha_d;
  array[N_e] vector<multiplier=sigma_e>[K] alpha_e;
  array[N_f] vector<multiplier=sigma_f>[K] alpha_f;
  array[J] vector<multiplier=sigma_jj>[K] alpha_jj;

  // Group-Level Preictors
  matrix[L,K] gamma;               // Predictor Coefficient Matrix
  array[K] corr_matrix[J] Omega;   // Prior correlation matrix
  array[K] vector<lower=0>[J] tau; // Prior correlation scale
  matrix[J,K] beta;                // Modeled group-level slopes


model {

  // Group-Level Effect Coefficients
  to_vector(gamma) ~ normal(0, 5);

  // Declare Local Scope for Vectorized Group-Level Slope Prior Means
  array[J] vector[K] u_gamma_jj;

  for (j in 1:J) {
    u_gamma_jj[j] = (u_jj[j] * gamma)';
  /// Loop over Submodels
  for (k in 1:K){
    /// Declare Hyperpriors
    tau[k] ~ cauchy(0, 2.5);
    Omega[k] ~ lkj_corr(2);
    // Group-Level Slopes
    to_vector(beta[,k]) ~ multi_normal(to_vector(u_gamma_jj[,k]), quad_form_diag(Omega[k], tau[k]));
  // Materialize Group Effects
  array[N_ind] row_vector[K] x_b;
  for (n in 1:N_ind){
    x_b[n] = beta[jj[n],];

  /// Varying Intercept Prior SDs
  {sigma_a, sigma_b, sigma_c, sigma_d, sigma_e, sigma_f, sigma_jj} ~ normal(0,1);

  // Varying Intercepts
  alpha_zero ~ normal(0,1);

  /// Single Intercept for dichotomous variables
  alpha_a ~ normal(0, sigma_a);
  alpha_b ~ normal(0, sigma_b);

  /// Varying Intercept Prior Declaration
  for (j in 1:J) {
    alpha_jj[j] ~ normal(0, sigma_jj);
  for (n in 1:N_c){
    alpha_c[n] ~ normal(0, sigma_c);

  for (n in 1:N_d){
    alpha_d[n] ~ normal(0, sigma_d);

  for (n in 1:N_e){
    alpha_e[n] ~ normal(0, sigma_e);

  for (n in 1:N_f){
    alpha_f[n] ~ normal(0, sigma_f);

  for (n in 1:N) {  
    y[n] ~ categorical_logit(alpha_zero +[alpha_a', -alpha_a'][a[n]]' + [alpha_b', -alpha_b'][b[n]]' + alpha_c[c[n]] + alpha_d[d[n]] + alpha_e[e[n]] + alpha_f[f[n]] + alpha_jj[jj[n]] + x_b[n]);


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