# MCMC sampling with high-dimensional data

I am currently running MCMC to sample a posterior distribution. The model used is Hierarchical Bayesian model, and my Stan program is below:


data {
int<lower=1> ncol;
int<lower=1> nrow;
vector[ncol] yH;
vector[nrow] x;
matrix[nrow, ncol] A;
vector[nrow] sigma_x;
vector[ncol] sigma_y;
#matrix[nrow,ncol] sigma_x; #covariance matrix for sampling yT from multiNormal(u_1, sigma_x)
#matrix[nrow,ncol] sigma_y; #covariance matrix for sampling x from multiNormal(u_2, sigma_y)
vector[nrow] epsilon;
}

parameters {
vector<lower = 0>[ncol] yT;
}

model {
yT ~ normal(yH, sigma_y); //prior
//yT ~ multi_normal(yH, sigma_y);

x ~ normal(A*yT + epsilon, sigma_x); //likelihood
//x ~ multi_normal(A*yT + epsilon, sigma_x);
}


Below is my Rcode to run the Stan model above:

library(rstan)
library(MASS)
#library(truncnorm)
library(bayesplot)
library(bridgesampling)
library(MCMCpack)
library(bayesplot)
library(ggplot2)
library(coda)
library(clusterGeneration)
require(MASS)

rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())

#data preparation for inputting into Stan
for(i in 1:15){
nrow = nrow(rte_m[[i]]);
ncol = ncol(rte_m[[i]]);
A <- as.matrix(rte_m[[i]]);
sigma_x <- as.vector(sample.int(10, nrow(kf_vect[[i]]), replace=TRUE))
sigma_y <- as.vector(eps_vect[[i]])
yH <- as.vector(dh_vect[[i]]$X); yT <- yH + as.vector(eps_vect[[i]]); epsilon <- sample.int(15, nrow(kf_vect[[i]]), replace=TRUE) x <- as.vector(as.matrix(rte_m[[i]])%*%yT) + epsilon iterations = 100; #input data into a list called stan_data # stan_data = list(nrow = nrow, ncol = ncol, # yH = yH, # x = x, epsilon = epsilon, # A = A, sigma_x = sigma_x, sigma_y = sigma_y); #input it into our Stan model file "stamodeling.stan" stanmodel1 <- stan_model(file = "stamodeling.stan", model_name = "stanmodel1"); #NUTS (No U-Turn) sampler to generate posterior distribution stanfit <- sampling(stanmodel1, data = list(ncol = ncol,nrow = nrow, yH = yH, x=x, epsilon = epsilon, A = A, sigma_x = sigma_x, sigma_y = sigma_y) ,iter=iterations, chains = 3, cores = 2, control = list(max_treedepth = 15)); print(i) #extract the result and plot graph posterior_yTtheta <- as.array(stanfit) file_name10 <- paste("Comparison_KFnnls_Day", i, ".jpeg") jpeg(file_name10) plot(yH, col=1, type='l', ylab="d", xlab = "Zone index", ylim=c(0,max(yT))) lines(colMeans(posterior_yTtheta[,2,]),col=2, type = 'l', ylab="d", xlab = "Zone index") lines(yT,col=3) lines(mean_kf[[i]], col=4, type = 'l', ylab = "d") legend(x = "topleft",legend = c("prior", "recovered", "true", "Kalman-Filter"), col=1:4, lty=c(1,1,1,1)) dev.off() fit_summary <- summary(stanfit) file_name6 = paste("resultsummary_KFnnls_ Day", i) jpeg(file_name6) print(fit_summary$summary)
dev.off()

color_scheme_set("mix-brightblue-gray")
file_name = paste("Rhatnnls_histogram_Day", i)
jpeg(file_name)
rhats <- rhat(stanfit)
rhat_plt <- mcmc_rhat_hist(rhats) + yaxis_text(hjust = 1) + ggtitle(paste("Rhatnnls_histogram_Day", i))
print(rhat_plt)
dev.off()
}


My question Can anyone please help aid me in some ways to speed up this sampling process?? As you can see from the code, I am doing MCMC sampling with high-dimensional, large dataset (matrix A might be 875\times 875). I let Rstudio run for 18 hours straight on a Mac Air with 8GB RAM and 65.7 GB available in memory, but it is unable to complete 3 chains with only 100 iterations. I even turn on options(mc.cores = parallel::detectCores()) and rstan_options(auto_write = TRUE), but nothing helps speed up the sampling process.

Another thing I found weird is that I frequently encountered hash mismatching so recompiling.... message, even though I always click on rstan_options(auto_write = TRUE) and check my dev.off() to make sure it is 1.

Here is the blog of activity monitor on my laptop:

Hi, without really understanding the problem you are working on, here are some tips to (hopefully) help you resolve the issue:

• The folk theorem says that computational problems often result from the model being poorly specified. My best bet is that the model does not actually fit the data well hindering efficient sampling. For example can you be sure the yT is necessarily > 0?
• Run the model with a smaller, fully simulated dataset and see if it works quickly and recovers the true values (see e.g. https://arxiv.org/abs/1709.01449 for some other workflow hints). Working with simulated data lets you iterate faster and notice problems, it is time well invested. Also if the simulated data look very different than real data it is a sign that you need to change your model.
1 Like

@martinmodrak: Thank you so much for your help. I figured out the issue by putting the entire problem and dataset onto Amazon’s AWS server and run on there. And yes, yT is necessarily >0 in my problem.

By the way, could you please help take a look at this model in the other post I just posted (Sampling error: Unrecoverable error evaluating the log probability at the initial value)?? I wonder if you can kindly let me know if my marginalization process was correct, in the sense that from the log_lambda I obtained, I somehow could get the sampled values for the posterior distribution given by (Poisson x Normal) = Prior x Likelihood (model I used is Hierarchical Bayesian).

The best way to test is with simulate data.

Our vectors are column vectors, so they have rows, not columns.

These need to be declared with constraints to preserve covariance structure. We prefer Cholesky factors of correlation matrices with scaling as described in the manual chapter on regression.

But whatever you do, estimating an 800 x 800 covariance matrix is going to be slow and also very hard to fit unless you have a ton of data.

That’s 8GB memory and 66GB of (solid state) disk. You do not want to exceed the 8GB and start swapping. Run a single chain at a time with CmdStan for most efficiency.