Unable to recover mixture between Wiener-diffusion and uniform distribution

Modeling
,
14

sorry to reopen this: I had the same problem and the thread was very helpful.

However, I’m trying to understand if the likelihood computation can be made more efficient. Along the way, I tried replacing the call to log_mix in the likelihood function with a call to log_sum_exp (since there are parts of the log_sum_exp formula that can be precomputed in the transformed data section). So instead of:

log_mix(lambda, 
        uniform_lpdf(y | min_rt, max_rt),
        wiener_lpdf(y | bs, ndt, bias, mu)

This:

 log_sum_exp(log(lambda) + uniform_lpdf(y | min_rt, max_rt),
                        log1m(lambda) + wiener_lpdf(y | bs, ndt, bias, mu));

However- this causes divergences again. Any idea why?

As a reminder - this is the likelihood function:

real wiener_diffusion2_lpdf(real y, real mu, real bs, 
                            real ndt, real bias, real lambda, 
                            int dec, real min_rt, real max_rt) {
  if (y < ndt) {
    return(log(lambda) + uniform_lpdf(y | min_rt, max_rt));
  } else {
    if (dec == 1) {
      return log_mix(lambda, 
        uniform_lpdf(y | min_rt, max_rt),
        wiener_lpdf(y | bs, ndt, bias, mu)
      );
    } else {
      return log_mix(lambda, 
        uniform_lpdf(y | min_rt, max_rt),
        wiener_lpdf(y | bs, ndt, 1 - bias, - mu)
      );
    }
  }
}
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