What's the difference between putting all the model formula into transformed parameters block and leaving the model formula into the model block?

Modeling
,
1

Dear Stan experts, I am a psychological student interested in cognitive model and I noticed people use RL model in Stan with different ways. Some people( rlssm package author), they built a vector that store the model’s prediciton data in transformed block, and there are another bunch of people, for example, the author of hbayesdm package, they put the model’s formula in model block. What’s the difference between the two way? Does one way is faster than the other way? Thanks.

Here is the two template I copied from rlssm package and hbayesdm package.

rlssm way

data {
	int<lower=1> N;									// number of data items
	int<lower=1> K;									// number of options
	int<lower=1> trial_block[N];					// trial within block
	vector[N] f_cor;								// feedback correct option
	vector[N] f_inc;								// feedback incorrect option
	int<lower=1, upper=K> cor_option[N];			// correct option
	int<lower=1, upper=K> inc_option[N];			// incorrect option
	int<lower=1> block_label[N];					// block label
	int<lower=-1,upper=1> accuracy[N];				// accuracy (0, 1)
	real initial_value;								// intial value for learning in the first block
	vector[2] alpha_priors;							// mean and sd of the alpha prior
	vector[2] sensitivity_priors;					// mean and sd of the sensitivity prior
}
transformed data {
	vector[K] Q0;
	Q0 = rep_vector(initial_value, K);
}
parameters {
	real alpha;
	real sensitivity;
}
transformed parameters {
	real log_p_t[N];								// trial-by-trial probability
	vector[K] Q;									// Q state values

	real PE_cor;
	real PE_inc;
	real Q_mean;

	real transf_alpha;
	real transf_sensitivity;

	transf_alpha = Phi(alpha);
	transf_sensitivity = log(1 + exp(sensitivity));

	for (n in 1:N) {
		if (trial_block[n] == 1) {
			if (block_label[n] == 1) {
				Q = Q0;
			} else {
				Q_mean = mean(Q);
				Q = rep_vector(Q_mean, K);
			}
		}
		PE_cor = f_cor[n] - Q[cor_option[n]];
		PE_inc = f_inc[n] - Q[inc_option[n]];

		log_p_t[n] = transf_sensitivity*Q[cor_option[n]] - log(exp(transf_sensitivity*Q[cor_option[n]]) + exp(transf_sensitivity*Q[inc_option[n]]));

		Q[cor_option[n]] = Q[cor_option[n]] + transf_alpha*PE_cor;
		Q[inc_option[n]] = Q[inc_option[n]] + transf_alpha*PE_inc;
	}
}
model {
	alpha ~ normal(alpha_priors[1], alpha_priors[2]);
	sensitivity ~ normal(sensitivity_priors[1], sensitivity_priors[2]);

	accuracy ~ bernoulli(exp(log_p_t));
}
generated quantities {
	vector[N] log_lik;

	{for (n in 1:N) {
		log_lik[n] = bernoulli_lpmf(accuracy[n] | exp(log_p_t[n]));
	}
	}
}
****

hbayesdm way
****
#include /pre/license.stan

data {
  int<lower=1> N;
  int<lower=1> T;
  int<lower=1, upper=T> Tsubj[N];
  int<lower=-1, upper=2> choice[N, T];
  real outcome[N, T];  // no lower and upper bounds
}
transformed data {
  vector[2] initV;  // initial values for EV
  initV = rep_vector(0.0, 2);
}
parameters {
// Declare all parameters as vectors for vectorizing
  // Hyper(group)-parameters
  vector[2] mu_pr;
  vector<lower=0>[2] sigma;

  // Subject-level raw parameters (for Matt trick)
  vector[N] A_pr;    // learning rate
  vector[N] tau_pr;  // inverse temperature
}
transformed parameters {
  // subject-level parameters
  vector<lower=0, upper=1>[N] A;
  vector<lower=0, upper=5>[N] tau;

  for (i in 1:N) {
    A[i]   = Phi_approx(mu_pr[1]  + sigma[1]  * A_pr[i]);
    tau[i] = Phi_approx(mu_pr[2] + sigma[2] * tau_pr[i]) * 5;
  }
}
model {
  // Hyperparameters
  mu_pr  ~ normal(0, 1);
  sigma ~ normal(0, 0.2);

  // individual parameters
  A_pr   ~ normal(0, 1);
  tau_pr ~ normal(0, 1);

  // subject loop and trial loop
  for (i in 1:N) {
    vector[2] ev; // expected value
    real PE;      // prediction error

    ev = initV;

    for (t in 1:(Tsubj[i])) {
      // compute action probabilities
      choice[i, t] ~ categorical_logit(tau[i] * ev);

      // prediction error
      PE = outcome[i, t] - ev[choice[i, t]];

      // value updating (learning)
      ev[choice[i, t]] += A[i] * PE;
    }
  }
}
generated quantities {
  // For group level parameters
  real<lower=0, upper=1> mu_A;
  real<lower=0, upper=5> mu_tau;

  // For log likelihood calculation
  real log_lik[N];

  // For posterior predictive check
  real y_pred[N, T];

  // Set all posterior predictions to 0 (avoids NULL values)
  for (i in 1:N) {
    for (t in 1:T) {
      y_pred[i, t] = -1;
    }
  }

  mu_A   = Phi_approx(mu_pr[1]);
  mu_tau = Phi_approx(mu_pr[2]) * 5;

  { // local section, this saves time and space
    for (i in 1:N) {
      vector[2] ev; // expected value
      real PE;      // prediction error

      // Initialize values
      ev = initV;

      log_lik[i] = 0;

      for (t in 1:(Tsubj[i])) {
        // compute log likelihood of current trial
        log_lik[i] += categorical_logit_lpmf(choice[i, t] | tau[i] * ev);

        // generate posterior prediction for current trial
        y_pred[i, t] = categorical_rng(softmax(tau[i] * ev));

        // prediction error
        PE = outcome[i, t] - ev[choice[i, t]];

        // value updating (learning)
        ev[choice[i, t]] += A[i] * PE;
      }
    }
  }
}

****
2

Variables declared in the TP block differ from those declared in the model block in two ways:

  1. Bounds are permitted in declarations in the TP block, in which case when the computed quantity falls outside it’s declared bounds, the sample is rejected.

  2. Quantities declared in the TP block are saved to file.

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3

Thanks for responding. Because of the first feature of TP block, the sampling speed might be slower?

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4

I wouldn’t anticipate that it would be noticeably slower, but feel free to give it a try both ways and report back on the relative performance

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5

If the sampling speed is noticeably slower, I suspect that it will be due to the time needed to write all of those transformed parameters to disk.

What will definitely be slower, though, is reading and manipulating the resulting MCMC draws. If you don’t need the transformed parameters saved and don’t need their bounds checked, put them in the model block!

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