Unserialize error


I’ve got a stan model running on a linux machine with 8 cores that runs ok (there were some divergent transitions) when I use a limited data set, but runs into problems when I try to use a larger dataset.

If I run with the larger set, Stan compiles but breaks at this point:

starting worker pid=25766 on localhost:11383 at 15:55:47.772
starting worker pid=25775 on localhost:11383 at 15:55:47.984
starting worker pid=25784 on localhost:11383 at 15:55:48.196
starting worker pid=25793 on localhost:11383 at 15:55:48.407

SAMPLING FOR MODEL 'stan_model_v3' NOW (CHAIN 1).

SAMPLING FOR MODEL 'stan_model_v3' NOW (CHAIN 2).

SAMPLING FOR MODEL 'stan_model_v3' NOW (CHAIN 3).

SAMPLING FOR MODEL 'stan_model_v3' NOW (CHAIN 4).
Error in unserialize(socklist[[n]]) : error reading from connection

Is this likely to be a memory/configuration error or more likely to be something wrong with the model?

Thanks for any advice!

The former. My guess is that it ran out of RAM when it started the fourth chain. Maybe only do two at a time?

How do I specify stan to run the chains two at a time or one at a time? Currently calling:

stan(file = './stan_model_v3.stan', data = standata, chains = 4, 
                 control = list(max_treedepth = 15,adapt_delta = 0.99)

cores = 2

Hi Ben,
I tried going down in cores and using a single core (which crashed the R session completely). I also tried increasing memory and monitoring it’s usage, it picks up when the program starts running but comes nowhere near the limit which makes me think there must be something wrong with the model itself.

I’m trying to run build a hierarchy into a random walk where I have confirmed accurate measurements at the start/end of the period and then noisy, irregularly-spaced measurements in between. I also have confirmed measurements at a lower district level at the start and end of the period, but no measurements in between. I’m trying to model a random walk for each district between the start and end measurements where a weighted average of the district measurements should equal the region estimate for each day and the region random walk should move from the starting measurement to the finishing measurement subject to the noisy measurements. Also there are two distinct time periods where the whole process repeats itself (new start/finish, etc).

Is there anything particularly egregious or bad practice that I’m doing here? Thanks for any input you can give me!

//read in dimensions
 int N; //number of measurments
 int p; //number of random walks to be estimated - measuring p distinct quantities for now uncorrelated
 int N_k; //number of distinct time periods - in this case there are 2
 int station_k_id; //number of measurement stations over N_k periods (if same station is used twice it is counted twice)
 int s[station_k_id]; //position of measurement in time, used to match measurements to mu
 int total_days; //number of days in all the time periods
 int n_days[N_k]; //number of days in each time period
 int day_index[N]; //index for matching measurements to correct place in mu

 int N_station; //number of unique stations
 int station_id[station_k_id]; //unique station id
  int last_day_index[N_k]; //index in mu for end of distinct time period

  matrix[N_k,p] mu_start; //starting values 
  matrix[N_k,p] mu_finish; //finishing values
  matrix[N,p] y; //measurements
  matrix[N,p] y_se; //se of measurements
  int R; //number of districts
  row_vector[R] weights; //weights of districts
  int district_day_index[R,total_days]; //index for district measurements in mu_district - row for each district
  matrix[N_k*R,p] mu_start_district; //confirmed starting values district
  matrix [N_k*R,p] mu_finish_district; //confirmed finishing values district
  int mu_finish_district_index[R*N_k];


  matrix[N_station,p] systematic_error_std;
  matrix[total_days,p] innovation;
  matrix[total_days*R,p] innovation2;
  real<lower=0> sigma;
  real<lower=0> sigma2;
  real<lower=0> sd_inflator[N_station];
  real<lower=0> fin_sd_std;
  real<lower=0> fin_sd_std2;


transformed parameters{
  matrix[total_days,p] mu; //regional estimate
  matrix[total_days*R,p] mu_district; //district estimate - only have starting and finishing values
  matrix[N_station,p] systematic_error = systematic_error_std/20; //systematic error in measurement stations
  real<lower=0> fin_sd = fin_sd_std/100;
  real<lower=0> fin_sd2 = fin_sd_std2/100;
        int pos = 1;
    for (r in 0:(R-1)){
      int st = 1;
      for (k in 1:N_k) {
        mu_district[pos,] = mu_start_district[st+r,];  // fixing starting period of estimate to be confirmed starting estimate
          for (t in (pos+1):(pos+n_days[k]-1)){ 
            for (i in 1:p){
            mu_district[t,i] = mu_district[t-1,i] + innovation2[t,i]*sigma2; //district random walk
            pos = pos + n_days[k];
            st = st + R;
    int pos = 1;
    for (k in 1:N_k) {
      mu[pos,] = mu_start[k,]; // fixing starting period of estimate to be confirmed starting estimate
      for (t in (pos+1):(pos+n_days[k]-1)){
        for (i in 1:p){
        mu[t,i]=dot_product(weights,mu_district[district_day_index[,t-1],i])+innovation[t,i]*sigma; //regional estimate is random walk of weighted average of district estimates
    pos = pos + n_days[k];


    to_vector(innovation) ~ student_t(4, 0, 1);
    to_vector(innovation2) ~ student_t(4, 0, 1);
    to_vector(systematic_error_std) ~ normal(0,1);
    sigma ~ normal(0,1);
    sigma2 ~ normal(0,1);
    sd_inflator ~ normal(1,5);
    // fixing final period of estimate to be very close to actual confirmed final estimate
     for (i in 1:p){
      mu_finish[,i] ~ normal(mu[last_day_index,i], fin_sd);
      mu_finish_district[,i] ~ normal(mu_district[mu_finish_district_index,i], fin_sd2);

// Measurements at the regional level
  int pos2= 1;
  for (k in 1:station_k_id) {
    for (i in 1:p){
    segment(y[,i], pos2, s[k]) ~ normal(mu[segment(day_index, pos2, s[k]),i]+systematic_error[station_id[k],i], sd_inflator[station_id[k]]*segment(y_se[,i], pos2, s[k]));
    pos2 = pos2 + s[k];