Description Usage Arguments Value Author(s) References See Also
Functions to construct proposal distributions for use with MCMC methods.
1 2 3 4 5 6 | mvn.diag.rw(rw.sd)
mvn.rw(rw.var)
mvn.rw.adaptive(rw.sd, rw.var, scale.start = NA, scale.cooling = 0.999,
shape.start = NA, target = 0.234, max.scaling = 50)
|
rw.sd |
named numeric vector; random-walk SDs for a multivariate normal random-walk proposal with diagonal variance-covariance matrix. |
rw.var |
square numeric matrix with row- and column-names. Specifies the variance-covariance matrix for a multivariate normal random-walk proposal distribution. |
scale.start, scale.cooling, shape.start, target, max.scaling |
parameters
to control the proposal adaptation algorithm. Beginning with MCMC
iteration |
Each of these calls constructs a function suitable for use as the
proposal
argument of pmcmc
or abc
. Given a parameter
vector, each such function returns a single draw from the corresponding
proposal distribution.
Aaron A. King, Sebastian Funk
Gareth O. Roberts and Jeffrey S. Rosenthal. Examples of Adaptive MCMC. J. Comput. Graph. Stat., 18:349–367, 2009.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.