Non-isotropic length scales for graph-based Gaussian processes

.github/workflows/main.yml

@@ -15,7 +15,7 @@ env:
   # Our tests may contain a number of stochastic elements. Setting a seed will make sure they are
   # not flaky (but also hide potential issues).
   SEED: "0"
-  cmdstanVersion: "2.31.0"
+  cmdstanVersion: "2.36.0"
 
 jobs:
   build:

stan/docs/interface/fft.rst

@@ -9,7 +9,7 @@ Utility Functions
 The following functions, and their two-dimensional analogues, primarily exist as utility functions but may be useful for more complex models.
 
 - :stan:func:`gp_rfft` transforms Gaussian process realizations to the Fourier domain and scales the coefficients such that they are white noise under the Gaussian process prior.
-- :stan:func:`gp_rfft_log_abs_det_jacobian` evaluates the log absolute determinant of the Jacobian associated with the transformations :stan:func:`gp_rfft`.
+- :stan:func:`gp_rfft_log_abs_det_jac` evaluates the log absolute determinant of the Jacobian associated with the transformations :stan:func:`gp_rfft`.
 
 Together, these two functions are used by :stan:func:`gp_rfft_lpdf` to evaluate the likelihood.
 

stan/gptools/stan/__init__.py

@@ -9,6 +9,17 @@ def get_include() -> str:
     """
     Get the include directory for the library.
     """
+    version = cmdstanpy.cmdstan_version()
+    if version and version < (2, 36):  # pragma: no cover
+        logging.warning(
+            "cmdstan<2.36.0 had a bug in the evaluation of Matern 3/2 kernels "
+            "with different length scales for different dimensions; see "
+            "https://github.com/stan-dev/math/pull/3084 for details. Your "
+            "model may yield unexpected results or crash if you use "
+            "nearest-neighbor Gaussian processes with Matern 3/2 kernels. Your "
+            "cmdstan version is %d.%d; consider upgrading.",
+            *version,
+        )
     return os.path.dirname(__file__)
 
 

stan/gptools/stan/gptools/fft1.stan

@@ -75,7 +75,7 @@ Evaluate the log absolute determinant of the Jacobian associated with
 :param n: Size of the real signal. Necessary because the size cannot be inferred from :code:`rfft`.
 :returns: Log absolute determinant of the Jacobian.
 */
-real gp_rfft_log_abs_det_jacobian(vector cov_rfft, int n) {
+real gp_rfft_log_abs_det_jac(vector cov_rfft, int n) {
     vector[n %/% 2 + 1] rfft_scale = gp_evaluate_rfft_scale(cov_rfft, n);
     return - sum(log(rfft_scale[1:n %/% 2 + 1])) -sum(log(rfft_scale[2:(n + 1) %/% 2]))
         - log(2) * ((n - 1) %/% 2) + n * log(n) / 2;
@@ -94,7 +94,7 @@ real gp_rfft_lpdf(vector y, vector loc, vector cov_rfft) {
     int n = size(y);
     int nrfft = n %/% 2 + 1;
     vector[n] z = gp_rfft(y, loc, cov_rfft);
-    return std_normal_lpdf(z) + gp_rfft_log_abs_det_jacobian(cov_rfft, n);
+    return std_normal_lpdf(z) + gp_rfft_log_abs_det_jac(cov_rfft, n);
 }
 
 

stan/gptools/stan/gptools/fft2.stan

@@ -140,7 +140,7 @@ Evaluate the log absolute determinant of the Jacobian associated with
     Fourier transform :code:`cov_rfft2`).
 :returns: Log absolute determinant of the Jacobian.
 */
-real gp_rfft2_log_abs_det_jacobian(matrix cov_rfft2, int width) {
+real gp_rfft2_log_abs_det_jac(matrix cov_rfft2, int width) {
     int height = rows(cov_rfft2);
     int n = width * height;
     int fftwidth = width %/% 2 + 1;
@@ -185,7 +185,7 @@ Evaluate the log probability of a two-dimensional Gaussian process realization i
 */
 real gp_rfft2_lpdf(matrix y, matrix loc, matrix cov_rfft2) {
     return std_normal_lpdf(to_vector(gp_rfft2(y, loc, cov_rfft2)))
-        + gp_rfft2_log_abs_det_jacobian(cov_rfft2, cols(y));
+        + gp_rfft2_log_abs_det_jac(cov_rfft2, cols(y));
 }