fix(ehgf): invalid eHGF update code

Author: bugoverdose

commons/stan_files/ehgf_ibrb.stan

@@ -208,7 +208,7 @@ model {
   sigma ~ normal(0,1);
 
   // individual parameters
-  mu0_pr ~ normal(0,1);
+  mu0_pr ~ normal(0,10);
   kappa_pr ~ normal(0,10);
   omega_pr ~ normal(0,10);
   zeta_pr ~ normal(0,10);
@@ -262,21 +262,22 @@ model {
         real mu_prev_lower = mu_hat[l-1];
         real sa_lower = sa[l-1];
         real sa_prev_lower = sa_hat[l-1];
+        real vv, pimhat, ww, rr, dd;
 
         real v = exp(ka * mu_prev_ + om); // volatility
         real w = v / sa_prev_lower;       // weighting factor (level: l-1)
 
         real vpe = ((sa_lower + pow(mu_lower - mu_prev_lower, 2)) / sa_prev_lower) - 1; // volatility prediction error
-        real lr = 0.5 * (1.0/sa_hat[l]) * ka * w; // learning rate
+        real lr = 0.5 * sa_hat[l] * ka * w; // learning rate
         real pwpe = lr * vpe;             // precision-weighted prediction error
+        mu[l-1] = mu_prev_ + pwpe;        // posterior prediction
 
-        real vv = exp(ka *mu [l-1] +om);
-        real pimhat = 1.0 / (sa_prev_lower +vv);
-        real ww = vv * pimhat;
-        real rr = (vv - sa_prev_lower) * pimhat;
-        real dd = (sa_lower + square(mu_lower - mu_prev_lower)) *pimhat - 1;
+        vv = exp(ka * mu[l-1] +om);
+        pimhat = 1.0 / (sa_prev_lower +vv);
+        ww = vv * pimhat;
+        rr = (vv - sa_prev_lower) * pimhat;
+        dd = (sa_lower + square(mu_lower - mu_prev_lower)) *pimhat - 1;
 
-        mu[l-1] = mu_prev_ + pwpe;        // posterior prediction
         sa[l] = 1.0/((1.0/sa_hat[l]) + fmax(0.0, 0.5 * square(ka) * ww * (ww + rr * dd)));
       }
 
@@ -350,21 +351,22 @@ generated quantities {
         real mu_prev_lower = mu_hat[l-1];
         real sa_lower = sa[l-1];
         real sa_prev_lower = sa_hat[l-1];
+        real vv, pimhat, ww, rr, dd;
 
         real v = exp(ka * mu_prev_ + om);// volatility
         real w = v / sa_prev_lower;      // weighting factor (level: l-1)
 
         real vpe = ((sa_lower + pow(mu_lower - mu_prev_lower, 2)) / sa_prev_lower) - 1; // volatility prediction error
-        real lr = 0.5 * (1.0/sa_hat[l]) * ka * w; // learning rate
+        real lr = 0.5 * sa_hat[l] * ka * w; // learning rate
         real pwpe = lr * vpe;             // precision-weighted prediction error
+        mu[l-1] = mu_prev_ + pwpe;        // posterior prediction
 
-        real vv = exp(ka *mu [l-1] +om);
-        real pimhat = 1.0 / (sa_prev_lower +vv);
-        real ww = vv * pimhat;
-        real rr = (vv - sa_prev_lower) * pimhat;
-        real dd = (sa_lower + square(mu_lower - mu_prev_lower)) *pimhat - 1;
+        vv = exp(ka * mu[l-1] +om);
+        pimhat = 1.0 / (sa_prev_lower +vv);
+        ww = vv * pimhat;
+        rr = (vv - sa_prev_lower) * pimhat;
+        dd = (sa_lower + square(mu_lower - mu_prev_lower)) *pimhat - 1;
 
-        mu[l-1] = mu_prev_ + pwpe;        // posterior prediction
         sa[l] = 1.0/((1.0/sa_hat[l]) + fmax(0.0, 0.5 * square(ka) * ww * (ww + rr * dd)));
       }
 

commons/stan_files/ehgf_ibrb_multipleB.stan

@@ -214,7 +214,7 @@ model {
   sigma ~ normal(0,1);
 
   // individual parameters
-  mu0_pr ~ normal(0,1);
+  mu0_pr ~ normal(0,10);
   kappa_pr ~ normal(0,10);
   omega_pr ~ normal(0,10);
   zeta_pr ~ normal(0,10);
@@ -269,21 +269,22 @@ model {
           real mu_prev_lower = mu_hat[l-1];
           real sa_lower = sa[l-1];
           real sa_prev_lower = sa_hat[l-1];
+          real vv, pimhat, ww, rr, dd;
 
           real v = exp(ka * mu_prev_ + om); // volatility
           real w = v / sa_prev_lower;       // weighting factor (level: l-1)
 
           real vpe = ((sa_lower + pow(mu_lower - mu_prev_lower, 2)) / sa_prev_lower) - 1; // volatility prediction error
-          real lr = 0.5 * (1.0/sa_hat[l]) * ka * w; // learning rate
+          real lr = 0.5 * sa_hat[l] * ka * w; // learning rate
           real pwpe = lr * vpe;             // precision-weighted prediction error
+          mu[l-1] = mu_prev_ + pwpe;        // posterior prediction
 
-          real vv = exp(ka *mu [l-1] +om);
-          real pimhat = 1.0 / (sa_prev_lower +vv);
-          real ww = vv * pimhat;
-          real rr = (vv - sa_prev_lower) * pimhat;
-          real dd = (sa_lower + square(mu_lower - mu_prev_lower)) *pimhat - 1;
+          vv = exp(ka * mu[l-1] +om);
+          pimhat = 1.0 / (sa_prev_lower +vv);
+          ww = vv * pimhat;
+          rr = (vv - sa_prev_lower) * pimhat;
+          dd = (sa_lower + square(mu_lower - mu_prev_lower)) *pimhat - 1;
 
-          mu[l-1] = mu_prev_ + pwpe;        // posterior prediction
           sa[l] = 1.0/((1.0/sa_hat[l]) + fmax(0.0, 0.5 * square(ka) * ww * (ww + rr * dd)));
         }
 
@@ -359,21 +360,22 @@ generated quantities {
           real mu_prev_lower = mu_hat[l-1];
           real sa_lower = sa[l-1];
           real sa_prev_lower = sa_hat[l-1];
+          real vv, pimhat, ww, rr, dd;
 
           real v = exp(ka * mu_prev_ + om);// volatility
           real w = v / sa_prev_lower;      // weighting factor (level: l-1)
 
           real vpe = ((sa_lower + pow(mu_lower - mu_prev_lower, 2)) / sa_prev_lower) - 1; // volatility prediction error
-          real lr = 0.5 * (1.0/sa_hat[l]) * ka * w; // learning rate
+          real lr = 0.5 * sa_hat[l] * ka * w; // learning rate
           real pwpe = lr * vpe;             // precision-weighted prediction error
+          mu[l-1] = mu_prev_ + pwpe;        // posterior prediction
 
-          real vv = exp(ka *mu [l-1] +om);
-          real pimhat = 1.0 / (sa_prev_lower +vv);
-          real ww = vv * pimhat;
-          real rr = (vv - sa_prev_lower) * pimhat;
-          real dd = (sa_lower + square(mu_lower - mu_prev_lower)) *pimhat - 1;
+          vv = exp(ka * mu[l-1] +om);
+          pimhat = 1.0 / (sa_prev_lower +vv);
+          ww = vv * pimhat;
+          rr = (vv - sa_prev_lower) * pimhat;
+          dd = (sa_lower + square(mu_lower - mu_prev_lower)) *pimhat - 1;
 
-          mu[l-1] = mu_prev_ + pwpe;        // posterior prediction
           sa[l] = 1.0/((1.0/sa_hat[l]) + fmax(0.0, 0.5 * square(ka) * ww * (ww + rr * dd)));
         }
 

commons/stan_files/ehgf_ibrb_vds.stan

@@ -183,7 +183,7 @@ model {
   sigma ~ normal(0,1);
 
   // individual parameters
-  mu0_pr ~ normal(0,1);
+  mu0_pr ~ normal(0,10);
   kappa_pr ~ normal(0,10);
   omega_pr ~ normal(0,10);
 
@@ -236,21 +236,22 @@ model {
         real mu_prev_lower = mu_hat[l-1];
         real sa_lower = sa[l-1];
         real sa_prev_lower = sa_hat[l-1];
+        real vv, pimhat, ww, rr, dd;
 
         real v = exp(ka * mu_prev_ + om); // volatility
         real w = v / sa_prev_lower;       // weighting factor (level: l-1)
 
         real vpe = ((sa_lower + pow(mu_lower - mu_prev_lower, 2)) / sa_prev_lower) - 1; // volatility prediction error
-        real lr = 0.5 * (1.0/sa_hat[l]) * ka * w; // learning rate
+        real lr = 0.5 * sa_hat[l] * ka * w; // learning rate
         real pwpe = lr * vpe;             // precision-weighted prediction error
+        mu[l-1] = mu_prev_ + pwpe;        // posterior prediction
 
-        real vv = exp(ka *mu [l-1] +om);
-        real pimhat = 1.0 / (sa_prev_lower +vv);
-        real ww = vv * pimhat;
-        real rr = (vv - sa_prev_lower) * pimhat;
-        real dd = (sa_lower + square(mu_lower - mu_prev_lower)) *pimhat - 1;
+        vv = exp(ka * mu[l-1] +om);
+        pimhat = 1.0 / (sa_prev_lower +vv);
+        ww = vv * pimhat;
+        rr = (vv - sa_prev_lower) * pimhat;
+        dd = (sa_lower + square(mu_lower - mu_prev_lower)) *pimhat - 1;
 
-        mu[l-1] = mu_prev_ + pwpe;        // posterior prediction
         sa[l] = 1.0/((1.0/sa_hat[l]) + fmax(0.0, 0.5 * square(ka) * ww * (ww + rr * dd)));
       }
 
@@ -324,21 +325,22 @@ generated quantities {
         real mu_prev_lower = mu_hat[l-1];
         real sa_lower = sa[l-1];
         real sa_prev_lower = sa_hat[l-1];
+        real vv, pimhat, ww, rr, dd;
 
         real v = exp(ka * mu_prev_ + om);// volatility
         real w = v / sa_prev_lower;      // weighting factor (level: l-1)
 
         real vpe = ((sa_lower + pow(mu_lower - mu_prev_lower, 2)) / sa_prev_lower) - 1; // volatility prediction error
         real lr = 0.5 * (1.0/sa_hat[l]) * ka * w; // learning rate
         real pwpe = lr * vpe;             // precision-weighted prediction error
+        mu[l-1] = mu_prev_ + pwpe;        // posterior prediction
 
-        real vv = exp(ka *mu [l-1] +om);
-        real pimhat = 1.0 / (sa_prev_lower +vv);
-        real ww = vv * pimhat;
-        real rr = (vv - sa_prev_lower) * pimhat;
-        real dd = (sa_lower + square(mu_lower - mu_prev_lower)) *pimhat - 1;
+        vv = exp(ka * mu[l-1] +om);
+        pimhat = 1.0 / (sa_prev_lower +vv);
+        ww = vv * pimhat;
+        rr = (vv - sa_prev_lower) * pimhat;
+        dd = (sa_lower + square(mu_lower - mu_prev_lower)) *pimhat - 1;
 
-        mu[l-1] = mu_prev_ + pwpe;        // posterior prediction
         sa[l] = 1.0/((1.0/sa_hat[l]) + fmax(0.0, 0.5 * square(ka) * ww * (ww + rr * dd)));
       }
 

commons/stan_files/ehgf_ibrb_vds_multipleB.stan

@@ -190,7 +190,7 @@ model {
   sigma ~ normal(0,1);
 
   // individual parameters
-  mu0_pr ~ normal(0,1);
+  mu0_pr ~ normal(0,10);
   kappa_pr ~ normal(0,10);
   omega_pr ~ normal(0,10);
 
@@ -244,21 +244,22 @@ model {
           real mu_prev_lower = mu_hat[l-1];
           real sa_lower = sa[l-1];
           real sa_prev_lower = sa_hat[l-1];
+          real vv, pimhat, ww, rr, dd;
 
           real v = exp(ka * mu_prev_ + om); // volatility
           real w = v / sa_prev_lower;       // weighting factor (level: l-1)
 
           real vpe = ((sa_lower + pow(mu_lower - mu_prev_lower, 2)) / sa_prev_lower) - 1; // volatility prediction error
-          real lr = 0.5 * (1.0/sa_hat[l]) * ka * w; // learning rate
+          real lr = 0.5 * sa_hat[l] * ka * w; // learning rate
           real pwpe = lr * vpe;             // precision-weighted prediction error
+          mu[l-1] = mu_prev_ + pwpe;        // posterior prediction
 
-          real vv = exp(ka *mu [l-1] +om);
-          real pimhat = 1.0 / (sa_prev_lower +vv);
-          real ww = vv * pimhat;
-          real rr = (vv - sa_prev_lower) * pimhat;
-          real dd = (sa_lower + square(mu_lower - mu_prev_lower)) *pimhat - 1;
+          vv = exp(ka * mu[l-1] +om);
+          pimhat = 1.0 / (sa_prev_lower +vv);
+          ww = vv * pimhat;
+          rr = (vv - sa_prev_lower) * pimhat;
+          dd = (sa_lower + square(mu_lower - mu_prev_lower)) *pimhat - 1;
 
-          mu[l-1] = mu_prev_ + pwpe;        // posterior prediction
           sa[l] = 1.0/((1.0/sa_hat[l]) + fmax(0.0, 0.5 * square(ka) * ww * (ww + rr * dd)));
         }
 
@@ -334,21 +335,22 @@ generated quantities {
           real mu_prev_lower = mu_hat[l-1];
           real sa_lower = sa[l-1];
           real sa_prev_lower = sa_hat[l-1];
+          real vv, pimhat, ww, rr, dd;
 
           real v = exp(ka * mu_prev_ + om);// volatility
           real w = v / sa_prev_lower;      // weighting factor (level: l-1)
 
           real vpe = ((sa_lower + pow(mu_lower - mu_prev_lower, 2)) / sa_prev_lower) - 1; // volatility prediction error
           real lr = 0.5 * (1.0/sa_hat[l]) * ka * w; // learning rate
           real pwpe = lr * vpe;             // precision-weighted prediction error
+          mu[l-1] = mu_prev_ + pwpe;        // posterior prediction
 
-          real vv = exp(ka *mu [l-1] +om);
-          real pimhat = 1.0 / (sa_prev_lower +vv);
-          real ww = vv * pimhat;
-          real rr = (vv - sa_prev_lower) * pimhat;
-          real dd = (sa_lower + square(mu_lower - mu_prev_lower)) *pimhat - 1;
+          vv = exp(ka * mu[l-1] +om);
+          pimhat = 1.0 / (sa_prev_lower +vv);
+          ww = vv * pimhat;
+          rr = (vv - sa_prev_lower) * pimhat;
+          dd = (sa_lower + square(mu_lower - mu_prev_lower)) *pimhat - 1;
 
-          mu[l-1] = mu_prev_ + pwpe;        // posterior prediction
           sa[l] = 1.0/((1.0/sa_hat[l]) + fmax(0.0, 0.5 * square(ka) * ww * (ww + rr * dd)));
         }
 

commons/stan_files/hgf_ibrb.stan

@@ -206,7 +206,7 @@ model {
   sigma ~ normal(0,1);
 
   // individual parameters
-  mu0_pr ~ normal(0,1);
+  mu0_pr ~ normal(0,10);
   kappa_pr ~ normal(0,10);
   omega_pr ~ normal(0,10);
   zeta_pr ~ normal(0,10);

commons/stan_files/hgf_ibrb_multipleB.stan

@@ -212,7 +212,7 @@ model {
   sigma ~ normal(0,1);
 
   // individual parameters
-  mu0_pr ~ normal(0,1);
+  mu0_pr ~ normal(0,10);
   kappa_pr ~ normal(0,10);
   omega_pr ~ normal(0,10);
   zeta_pr ~ normal(0,10);