# Reparametrization of Gaussian process model with measurement error

I am trying to fit a Gaussian process quantile regression with measurement error but I am struggling with divergent transitions and was hoping if anyone has some suggestions in terms of reparametrization. To find out where my problem was I started with the simplest model (a quantile regression) and added more features. These are: `model1.stan` (just a quantile regression), `model2.stan` (a quantile regression with measurement error), `model3.stan` (gaussian process quantile regression), and `model4.stan` (gaussian process quantile regression with measurement error). Models 1 and 2 worked ok (though model 2 had some high R-hats), but models 3 and 4 ran very slowly (which is expected I guess), had large number of divergent transitions, high R-hats, etcâ€¦ I have attached a simulated dataset, the stan codes, and an R script for running those, and pasted below model 4. Adjusting `adapt_delta` and `max_treedepth` did not make any difference, so I suspect this really needs to be reparametrised but not sure where to startâ€¦ Any tips/advice will be greatly appreciated!

``````functions{
int which_first(real z, vector x){
int index = 1;
for (i in 1:rows(x))
{
if (z > x[i]) {break;}
else {index += 1;}
}
return(index);
}
vector linear_interpolation_v(vector z, vector x, vector y) {
int J = rows(z);
vector[J] ans;
for (j in 1:J)
{
int index=which_first(z[j],x);
ans[j] = y[index] + (((y[index - 1] - y[index]) * (z[j] - x[index]))/(x[index - 1] - x[index]));
}
return ans;
}
real rho_quantile(real y, real quantile) {
if (y < 0) {
return y * (quantile - 1);
} else {
return y * quantile;
}
}
real asym_laplace_lpdf(real y, real mu, real sigma, real quantile) {
return log(quantile * (1 - quantile)) -
log(sigma) -
rho_quantile((y - mu) / sigma, quantile);
}
real asym_laplace_lcdf(real y, real mu, real sigma, real quantile) {
if (y < mu) {
return log(quantile) + (1 - quantile) * (y - mu) / sigma;
} else {
return log1m((1 - quantile) * exp(-quantile * (y - mu) / sigma));
}
}
real asym_laplace_lccdf(real y, real mu, real sigma, real quantile) {
if (y < mu) {
return log1m(quantile * exp((1 - quantile) * (y - mu) / sigma));
} else {
return log1m(quantile) - quantile * (y - mu) / sigma;
}
}
matrix cov_GPL2(matrix x, real sq_alpha, real sq_rho, real delta) {
int N = dims(x)[1];
matrix[N, N] K;
for (i in 1:(N-1)) {
K[i, i] = sq_alpha + delta;
for (j in (i + 1):N) {
K[i, j] = sq_alpha * exp(-sq_rho * square(x[i,j]) );
K[j, i] = K[i, j];
}
}
K[N, N] = sq_alpha + delta;
return K;
}
}
data{
int<lower=1> N;
vector[N] X; //D
matrix[N,N] Dmat;
real<lower=0,upper=1> quantile;
vector[N] cra; //Observed CRA
vector[N] cra_error; // Observed Calibration Error
int<lower=0> tau; // Number of raw in the calibration curve
vector[tau] calBP; //Calibration curve
vector[tau] c14BP; //Calibration curve
vector[tau] c14_err; //Calibration curve
}
transformed data {
real means_X = mean(X);
vector[N] Xc = X - means_X;
vector[N] cra_error_sq = square(cra_error);
}
parameters{
vector<lower=0,upper=55000>[N] theta;
vector[N] s;
real alpha;
real beta;
real<lower=0> sigma;
real<lower=0> etasq;
real<lower=0> rhosq;
}
model{
matrix[N,N] SIGMA;
matrix[N,N] L_SIGMA;
vector[N] mu;
target += exponential_lpdf(sigma |  0.05);
target += exponential_lpdf(rhosq |  50);
target += exponential_lpdf(etasq |  10);
target += normal_lpdf(beta | 0, 0.5);
target += normal_lpdf(alpha | 3000, 200);
SIGMA = cov_GPL2(Dmat, etasq, rhosq, 0.001);
L_SIGMA = cholesky_decompose(SIGMA);
target += multi_normal_cholesky_lpdf(s|rep_vector(0,N),L_SIGMA);
target += normal_lpdf(cra|linear_interpolation_v(theta,calBP,c14BP),sqrt( cra_error_sq + square(linear_interpolation_v(theta,calBP,c14_err))));
for (n in 1:N) {
mu[n] = alpha + Xc[n] * (beta+s[n]);
target += asym_laplace_lpdf(theta[n] | mu[n], sigma, quantile);
}

}
generated quantities {
// actual population-level intercept
real b_Intercept = alpha - (means_X * beta); //addresses the centering
}
``````

runscript.R (4.4 KB)
x.csv (4.3 KB)
intcal20.csv (211.9 KB)
model4.stan (3.3 KB)
model3.stan (2.3 KB)
model1.stan (2.9 KB)
model2.stan (3.9 KB)

3 Likes

Hi,
sorry for not getting to you earlier, this is a good question and sharing all the models and code for simulation is great!

I would start the investigation there - in my experience it is not unusual that what appears as a relatively mild issue in a model could be the cause of big problems once you add additional layers of complexity.

In particular, I am a bit suspicious about the linear interpolation method you use, because it makes the derivative of the log density not continuous in `theta`. Note that there are interpolation techniques that will give you continuous derivatives - I have experience with a kernel smoother and B-splines (or any other spline basis) which both work neatly and can be easily implemented in Stan (and without any need for hacks like `which_first`. Feel free to ask for clarification if you are unsure how to implement those in Stan.

Second problem is probably the Gaussian process - I would first try to build the GP as a separate model to make sure it works well. A possible problem in your case is that your prior seems to allow arbitrarily small lengthscale, but you usually want to avoid lengthscale smaller than the resolution of your observed data. See Mike Betancourtâ€™s case study for more details and insights into fitting GPs reliably: https://betanalpha.github.io/assets/case_studies/gaussian_processes.html (the discussion of priors for length scale in section 3.2.1 and below)

Best of luck with your model!

2 Likes

Hi @martinmodrak ,
Many thanks for the pointers.

I am a bit suspicious about the linear interpolation method you use, because it makes the derivative of the log density not continuous in `theta` .

A-ha, I see why there is so no build-in interpolation function like in JAGS, presumably because of HMC? Could you point me to a stan function yielding continuous derivatives with the same arguments as `linear_interpolation_v`?

Second problem is probably the Gaussian process - I would first try to build the GP as a separate model to make sure it works well.

Gotcha - Changing the prior of `rhosq` to an inverse gamma did remove the divergent transitions in model3 (though I still have low BFMI). I will explore this more thoroughly after reading your link. Thanks again!

I donâ€™t think there is a standard one. Hereâ€™s a code for a kernel smoother (with gaussian kernel) I wrote a few years ago where `time` is the new point you want to compute and `value_times` and `values` are the x-y values you are smoothing over:

`````` real kernel_estimate(real time, vector value_times, vector values, real bandwidth) {
int num_values = num_elements(values);
vector[num_values] weights = exp(-0.5* square((value_times - time)  / bandwidth));
return sum(weights .* values) / sum(weights);
}
``````

This can however be quite inefficient for larger number of points as you do a relatively expensive computation for each of the known points.

if you want to use splines (which might be more efficient), then the simplest approach would IMHO be to fit a spline in R and then pass the fitted coefficients to Stan where you would evaluate the spline using de Boor algorithm.
Since you work with a vector of points at a time, you might benefit from evaluating the spline basis in one go for all the values as in Splines In Stan . Once you evaluate the basis at the points of interest (`theta`), you then compute the final value as a weighted sum of the individual basis functions, where weights are the fitted coefficients from R. In both cases you need to make sure that you are in fact using exactly the same basis both in the R code and the Stan code.

Hope that clarifies more than confuses :-)

2 Likes

Thanks! I see what you mean - The kernel smoother works efficiently but it does seem to take way too long. Will give a crack with the B-spline and post something if I manage to make it work! (reading various github thread (e.g. this one) I see why there is no equivalent of BUGâ€™s `interp.lin` in stanâ€¦ )

1 Like