gp_log_marg_like_helper: Compute the log marginal likelihood from parameters of the GP
In ebenmichael/gaussianProcess: Fits Gaussian Processes for Regression
Description
Usage
Arguments
Value
Description
Compute the log marginal likelihood from parameters of the GP
Usage
1
gp_log_marg_like_helper(y, K, alpha)
Arguments
y
An n dimensional vector of outputs for observed data
K
The n x n covariance matrix on the seen data
alpha
An n dimensional vector, alpha = K^-1 y
Value
Log marginal likelihood
ebenmichael/gaussianProcess documentation built on May 15, 2019, 7:30 p.m.
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Description Usage Arguments Value
Description
Compute the log marginal likelihood from parameters of the GP
Usage
1 | gp_log_marg_like_helper(y, K, alpha)
|
Arguments
y |
An n dimensional vector of outputs for observed data |
K |
The n x n covariance matrix on the seen data |
alpha |
An n dimensional vector, alpha = K^-1 y |
Value
Log marginal likelihood
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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