Description Usage Arguments Details Value References See Also
Generate a sample from a probability distribution with slice sampling with univariate steps along eigenvectors.
1 2 3 4 | univar.eigen.sample(target.dist, x0, sample.size, tuning=1,
steps.out=100, cheat=FALSE)
cheat.univar.eigen.sample(target.dist, x0, sample.size, tuning=1,
steps.out=100)
|
target.dist |
Target distribution; see |
x0 |
Numeric vector containing initial state. |
sample.size |
Sample size requested. |
tuning |
Initial slice approximation length. |
steps.out |
Maximum number of iterations the stepping out algorithm should run when choosing an initial slice approximation. Set to NULL to refrain from stepping out. |
cheat |
Set to true to use the covariance from target.dist instead of estimating it. This is not possible on real problems but can be useful for debugging. |
These two functions implement slice sampling with univariate steps
along estimated eigenvectors. Thompson (2011, ch. 3) has details
on the algorithms. The functions follow the interface used by
compare.samplers
. Calling cheat.univar.eigen.sample
is equivalent to calling univar.eigen.sample
with
cheat=TRUE
; it is provided as a convenience so that it can
be passed directly to compare.samplers
.
A list with elements X
, evals
, and grads
.
See compare.samplers
for more information.
Thompson, M. B. (2011), Slice Sampling with Multivariate Steps. http://hdl.handle.net/1807/31955.
compare.samplers
,
oblique.hyperrect.sample
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