Generating random values from a user-defined distribution

Modeling
1

Hello, everyone!

I am a fresher about Stan, especially Rstan. Recently, I have been thinking about a question which puzzled me a lot:

Suppose X\sim f(x) where f(x) is the probability density function of r.v. X, which does not have the usual distributions expressions, such as Normal, Gamma, but a complex expression. And now I want to sample some random values from this distribution. How can I finish this job?

In Rstan, I set the stan code like this:

parameters {
  real x;
}
model {
  x ~ f(x);
}

But it doesn’t work. Can anyone help me?
I am really appreciate for your kind help!!

2

In the function declaration call it f_lpdf to use the ~ syntax with custom distributions.

1 Like
3

It is much obliged for your reply. And according to your answer, i rewrite some codes with sampling from a user defined distribution. Is this right?

code='
  functions {
     real uf_lpdf(real y) {
         return 1/3*exp(-1/3*y);
     }
  }
  parameters {
     real<lower=0> k;
  }
  model {
    k ~ uf_lpdf( );
  }'
fit <- stan_model(model_code=code)

Thanks a lot!

4

If I recall correctly, you don’t need the _lpdf in the model block. Your Stan model would look like:

functions {
  real uf_lpdf(real y) {
    return ((1/3) * exp(-(1/3) * y));
  }
}

data {
}

parameters {
  real <lower = 0> k;
} 

model {
  k ~ uf();
}
1 Like
5

Thanks, and I changed my Stan code based on your idea. However, some new problems arose~

You see that Y \sim exp(\lambda) where \lambda=3 in my Stan code.

### Trunk A
functions {
    real uf_lpdf(real y) {
    return (1/3 * exp(-(1/3) * y));
  }
}

So equivalently, results with above code should be the same with the following code:

##Trunk B
functions {
    real uf_lpdf(real y) {
    return exponential(y | (1/3));
  }
}

But, the code with Trunk A has some warnings:

1: There were 5150 divergent transitions after warmup. Increasing adapt_delta above 0.8 may help. See
http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup 
2: Examine the pairs() plot to diagnose sampling problems

and the outcome is unbelievable:

       mean se_mean   sd          2.5%           25%
k    9.075119e+307     NaN  Inf 5.540203e+306 4.726369e+307
lp__  7.088200e+02    0.03 0.94  7.063000e+02  7.084500e+02

while the outcome with Trunk B is normal:


      mean se_mean   sd  2.5%   25%   50%   75% 97.5% n_eff Rhat
k     0.34    0.01 0.34  0.01  0.09  0.23  0.48  1.25  1332 1.00
lp__ -1.59    0.03 0.82 -3.85 -1.80 -1.28 -1.06 -1.00   767 1.01

Do you know why the outcomes are so different?

6

This is the probability density function, not the log probability density function. The unnormalised log probability density function of the exponential distribution with rate parameter \lambda = 3 is:

functions { 
  real uf_lpdf(real y) { 
  return (-(1/3) * y); 
  }
}
2 Likes
7

I got it!
Thanks a lot!!