Description Usage Arguments Details Value See Also Examples
Construct a rational quadratic kernel.
1 |
columns |
a string vector giving the names of features on which the kernel acts. These will be used to access columns from a dataframe when the kernel is later used in a GP model. |
sigma |
a positive scalar parameter giving (the square-root of) the overall variance of the kernel |
l |
a positive scalar or vector parameter giving the characteristic lengthscale
of the kernel (how rapidly the covariance decays with difference in the
value of the covariate). Larger values of |
alpha |
a positive scalar or vector parameter controlling the roughness
of the GPs drawn from the kernel. Larger values of |
The rational quadratic kernel takes the form:
k_{rq}(\mathbf{x}, \mathbf{x}') = σ^2 ≤ft(1 + \frac{\mathbf{r} ^ 2}{α} \right) ^ {-α}
\mathbf{r} = {√{∑\limits_{d=1}^D ≤ft(\frac{(x_d - x_d')}{2l_d^2}\right) ^ 2}}
where \mathbf{x} are the covariates on which the kernel is active, l_d are the characteristic lengthscales for each covariate (column) x_d, σ^2 is the overall variance and α controls the roughness.
Larger values of l_i correspond to functions in which change less rapidly over the values of the covariates.
A kernel object for which there are a range of associated functions, see kernel
and access
for details.
Other kernel.constructors: composition
,
expo
, iid
, int
,
lin
, mat32
,
mat52
, per
, rbf
1 2 3 4 5 6 7 8 9 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.