hmflatprobit

This random walk Metropolis-Hastings algorithm takes advantage of the
availability of the maximum likelihood estimator (available via the <code>glm</code>
function) to center and scale the random walk in an efficient manner.

Metropolis-Hastings algorithm

random walk proposal

probit model

flat prior

Allows the reenactment of the R programs used in
the book Bayesian Essentials with R without further programming.
R code being available as well, they can be modified by the user
to conduct one's own simulations.
Marin J.-M. and Robert C. P. (2014) <doi:10.1007/978-1-4614-8687-9>.

JeanMichel Marin

bayess

Bayesian Essentials with R

hmflatprobit function

<dl> <dt>niter</dt>
<dd>number of iterations</dd> <dt>y</dt>
<dd>binary response variable</dd> <dt>X</dt>
<dd>covariates</dd> <dt>scale</dt>
<dd>scale of the random walk</dd></dl>

Arguments

Metropolis-Hastings for the probit model under a flat prior — hmflatprobit

<dl>

 <dt>niter</dt>
<dd>number of iterations</dd>

 <dt>y</dt>
<dd>binary response variable</dd>

 <dt>X</dt>
<dd>covariates</dd>

 <dt>scale</dt>
<dd>scale of the random walk</dd>

</dl>

hmflatprobit: Metropolis-Hastings for the probit model under a flat prior

Description

Usage

Value

Arguments

See Also

Examples