l.double.prime: The second derivative of a Gaussian process with respect to...
In spatial.gev.bma: Hierarchical spatial generalized extreme value (GEV) modeling with Bayesian Model Averaging (BMA)
Description
Usage
Arguments
Value
Author(s)
Description
This utility returns the value of the second derivative of
the posterior of lambda in a Gaussian process. It is used to make a
focused M-H proposal when updating the lambda parameter.
Usage
1
l.double.prime(tau, alpha, lambda, D, a, b)
Arguments
tau
The vector of current random effects.
alpha
The current state of the precision of the Gaussian process
lambda
The current state of lambda
D
The distance matrix of the relevant entries in tau
a
The prior parameter for lambda
b
The second prior parameter for lambda
Value
This returns a scalar that is then used in the focused update. Its a
pretty internal function.
Author(s)
Alex Lenkoski <alex@nr.no>
spatial.gev.bma documentation built on May 1, 2019, 6:34 p.m.
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Description Usage Arguments Value Author(s)
Description
This utility returns the value of the second derivative of the posterior of lambda in a Gaussian process. It is used to make a focused M-H proposal when updating the lambda parameter.
Usage
1 | l.double.prime(tau, alpha, lambda, D, a, b)
|
Arguments
tau |
The vector of current random effects. |
alpha |
The current state of the precision of the Gaussian process |
lambda |
The current state of lambda |
D |
The distance matrix of the relevant entries in tau |
a |
The prior parameter for lambda |
b |
The second prior parameter for lambda |
Value
This returns a scalar that is then used in the focused update. Its a pretty internal function.
Author(s)
Alex Lenkoski <alex@nr.no>
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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