Generalized Dirichlet Distribution


Can stan/Rstan generate Generalized Dirichlet Distribution, which is a generalization of Dirichlet Distribution for simplex data?


You can implement the Generalized Dirichlet distribution as a Stan function in your Stan program but it is not implemented in the Stan Math library.


The Wikipedia page for this the generalized Dirichlet needs some serious work. But it’s a simple enough form that it’d be easy to implement as a function directly in Stan. See the manual chapter on adding functions. Or you can just add the log density straight to the target.


There is also a case study showing how to add user defined distributions


Thank you all for the reply. I try to code the generalized Dirichlet by simply defining transformed parameters as Wiki suggest the generalized Dirichlet can be constructed by stick-breaking process. The code is modified by other people’s LDA implementation.

In R, I call the following functions, which is (generalized) LDA with 4 topics (since for my application the word position does not matter, hence I do some collapse. ), alpha is hyper parameter for topics and gamma1 and gamma2 are for generalized Dirichlet.

The code run with error as follows, and I wonder what is wrong with my implementation.
Thank you very much for your help.

" Rejecting initial value:
** Log probability evaluates to log(0), i.e. negative infinity.**
** Stan can’t start sampling from this initial value.**

Initialization between (-2, 2) failed after 100 attempts. **
** Try specifying initial values, reducing ranges of constrained values, or reparameterizing the model.


The following part is the code for R:

stan_data <- list(
K = 4,
V = ncol(x),
D = nrow(x),
n = x,
alpha = rep(1, 4),
gamma1 = rep(0.5, ncol(x)-1),
gamma2 = 0.5*(ncol(x) - seq(1:(ncol(x)-1)))

f3 <- stan_model(file = “lda_counts2.stan”)

stan_fit <- vb(
data = stan_data,
output_samples = 10,
eta = 1,
adapt_engaged = FALSE

This is in “lda_count2.stan”:

data {
  int<lower=1> K; // num topics
  int<lower=1> V; // num words
  int<lower=0> D; // num docs
  int<lower=0> n[D, V]; // word counts for each doc

  // hyperparameters
  vector<lower=0>[K] alpha;
  vector<lower=0>[V-1] gamma1;
  vector<lower=0>[V-1] gamma2;

parameters {
  simplex[K] theta[D]; // topic mixtures
  vector<lower=0,upper=1>[V-1] zeta[K]; // 

transformed parameters {
  simplex[V] beta[K];
  for (k in 1:K) {
	real tmpsum=0;
	beta[k,1] =  zeta[k,1];
    tmpsum =  zeta[k,1];
    for (m in 2:(V - 1)) {
		beta[k,m] = (1 - tmpsum)*zeta[k,m];
		tmpsum += beta[k,m];
    beta[k,V] = 1 - tmpsum;

model {
  for (d in 1:D) {
    theta[d] ~ dirichlet(alpha);

  for (k in 1:K) {
    for (m in 1:(V - 1)) {
		zeta[k,m] ~ beta(gamma1[m],gamma2[m]);

  for (d in 1:D) {
    vector[V] eta;
    eta = beta[1] * theta[d, 1];

    for (k in 2:K) {
      eta = eta + beta[k] * theta[d, k];
    n[d] ~ multinomial(eta);


[edit: escape model code]


Was there no other error message about what was failing? Are you using the latest version of Stan (we used to swallow some messages, and may still be swallowing some in RStan in 2.17).

Otherwise, you need to track through your program and figure out where the target density goes off the rails. Just drop in print statements like print("a ", target()); to print out the target density. It should always be finite.