| kernel | R Documentation |
These have STAN_kernel_* counterparts. These R versions
are provided for reference and are not optimized for speed. These are
used when generating simulated data, and not during model inference.
kernel_eq(x1, x2, alpha = 1, ell)
kernel_ns(x1, x2, alpha = 1, ell, a)
kernel_zerosum(x1, x2, M)
kernel_bin(x1, x2, pos_class = 0)
kernel_cat(x1, x2)
kernel_varmask(x1, x2, a, vm_params)
kernel_beta(beta, idx1_expand, idx2_expand)
x1 |
vector of length |
x2 |
vector of length |
alpha |
marginal std (default = 1) |
ell |
lengthscale |
a |
steepness of the warping function rise |
M |
number of categories |
pos_class |
binary (mask) kernel function has value one if both inputs have this value, other wise it is zero |
vm_params |
vector of two mask function parameters. |
beta |
a parameter vector (row vector) of length |
idx1_expand |
integer vector of length |
idx2_expand |
integer vector of length |
A matrix of size n x m.
kernel_eq(): Uses the exponentiated quadratic kernel.
kernel_ns(): Uses the non-stationary kernel (input warping + squared
exponential).
kernel_zerosum(): Uses the zero-sum kernel. Here, x1 and
x2 must be integer vectors (integers denoting different categories).
Returns a binary matrix.
kernel_bin(): Uses the binary (mask) kernel. Here, x1 and
x2 must be integer vectors (integers denoting different categories).
Returns a binary matrix.
kernel_cat(): Uses the categorical kernel. Here, x1 and
x2 must be integer vectors (integers denoting different categories).
Returns a binary matrix.
kernel_varmask(): Computes variance mask multiplier matrix. NaN's
in x1 and x2 will be replaced by 0.
kernel_beta(): Computes the heterogeneity multiplier matrix.
NOTE: idx_expand needs to be given so that
idx_expand[j]-1 tells the index of the beta parameter that should be
used for the jth observation. If observation j doesn't
correspond to any beta parameter, then idx_expand[j] should be 1.
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