resample

Perform residual resampling to the result of importance sampling

 misc 

The iterLap (iterated Laplace approximation) algorithm approximates a
general (possibly non-normalized) probability density on R^p, by repeated
Laplace approximations to the difference between current approximation
and true density (on log scale). The final approximation is a mixture of
multivariate normal distributions and might be used for example as a
proposal distribution for importance sampling (eg in Bayesian applications).
The algorithm can be seen as a computational generalization of the Laplace
approximation suitable for skew or multimodal densities.

Bjoern Bornkamp

iterLap

Approximate Probability Densities by Iterated Laplace
Approximations

resample function

<dl> <dt>n</dt>
<dd>Number of resamples to draw</dd> <dt>obj</dt>
<dd>An object of class <code>IS</code>, as produced by the <code>IS</code> function</dd></dl>

Arguments

Author

Residual resampling — resample

<dl>

 <dt>n</dt>
<dd>Number of resamples to draw</dd>

 <dt>obj</dt>
<dd>An object of class <code>IS</code>, as produced by the <code>IS</code> function</dd>

</dl>

resample: Residual resampling

Description

Usage

Value

Arguments

Author

Examples