sns.fghEval.numaug | R Documentation |
Augmenting a log-density with numerical gradient and Hessian, so it can be used by sns
or sns.run
. This augmentation will also be done inside the function, if the value of numderiv
parameter passed to sns
and sns.run
is 1
or 2
. The advantage of using sns.fghEval.numaug
outside these functions is efficiency, since the agumentation code will not have to be executed in every function call. Users must set numderiv
to 0
when calling sns
or sns.run
if calling sns.fghEval.numaug
first. See example.
sns.fghEval.numaug(fghEval, numderiv = 0 , numderiv.method = c("Richardson", "simple") , numderiv.args = list())
fghEval |
Log-density to be sampled from. A valid log-density can have one of 3 forms: 1) return log-density, but no gradient or Hessian, 2) return a list of |
numderiv |
This must be matched with |
numderiv.method |
Method used for numeric differentiation. This is passed to the |
numderiv.args |
Arguments to the numeric differentiation method chosen in |
A function, accepting same arguments as fghEval
, but guaranteed to return the original log-density, plus gradient and Hessian (both of which could possibly by numerically calculated). If numderiv=0
, fghEval
is returned without change. The function will return log-density, gradient and Hessian as elements f
, g
and h
of a list.
See package vignette for more details on SNS theory, software, examples, and performance.
Alireza S. Mahani, Asad Hasan, Marshall Jiang, Mansour T.A. Sharabiani
Mahani A.S., Hasan A., Jiang M. & Sharabiani M.T.A. (2016). Stochastic Newton Sampler: The R Package sns. Journal of Statistical Software, Code Snippets, 74(2), 1-33. doi:10.18637/jss.v074.c02
sns
, sns.run
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