Optimization of the hyperparameters
Description
Optimization of the hyperparameters using a sequence of subfunctions.
Usage
1 2 3 4 5  optimal_params (expt, LoF, start_hp, option = "a", ...)
optimal_B (expt, LoF, start_hp, option = "a", verbose=FALSE, ...)
optimal_identical_B(expt, LoF, start_hp, verbose=FALSE, ...)
optimal_diag_M (expt, LoF, start_hp)
optimal_M (expt, LoF, start_hp, ...)

Arguments
expt 
Object of class 
LoF 
List of functions 
start_hp 
Start value for the hyperparameters, an object of class 
option 
In function

verbose 
In function 
... 
Further arguments passed to the optimization routine 
Details
The userfriendly wrapper function is optimal_params()
. This
calls function optimal_B()
first, as most of the analysis is
conditional on B
. Then optimal_diag_M()
is called; this
places the maximum likelihood estimate for sigma^2 on
the diagonal of M
. Finally, optimal_M()
is called,
which assigns the offdiagonal elements of M
.
Each of the subfunctions returns an object appropriate for insertion
into a mhp
object.
The “meat” of optimal_params()
is
1 2 3 4 
See how object out
is modified sequentially, it being used as a
start point for the next function.
Value
Returns a mhp
object.
Note
Function optimal_diag_M()
uses MLEs for the diagonals, but using
each type of observation separately. It is conceivable that there is
information that is not being used here.
Author(s)
Robin K. S. Hankin
Examples
1 2 3 