Initial Values

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Description

This function returns the most recent posterior samples from an object of class demonoid or demonoid.hpc, the posterior means of an object of class iterquad, the posterior modes of an object of class laplace or vb, the posterior means of an object of class pmc with one mixture component, or the latest means of the importance sampling distribution of an object of class pmc with multiple mixture components. The returned values are intended to be the initial values for future updates.

Usage

1

Arguments

x

This is an object of class demonoid, demonoid.hpc, iterquad, laplace, pmc, or vb.

Details

Unless it is known beforehand how many iterations are required for IterativeQuadrature, LaplaceApproximation, or VariationalBayes to converge, MCMC in LaplacesDemon to appear converged, or the normalized perplexity to stabilize in PMC, multiple updates are necessary. An additional update, however, should not begin with the same initial values as the original update, because it will have to repeat the work already accomplished. For this reason, the as.initial.values function may be used at the end of an update to change the previous initial values to the latest values.

When using LaplacesDemon.hpc, as.initial.values should be used when the output is of class demonoid.hpc, before the Combine function is used to combine the multiple chains for use with Consort and other functions, because the Combine function returns an object of class demonoid, and the number of chains will become unknown. The Consort function may suggest using as.initial.values, but when applied to an object of class demonoid, it will return the latest values as if there were only one chain.

Value

The returned value is a vector (or matrix in the case of an object of class demonoid.hpc, or pmc with multiple mixture components) of the latest values, which may now be used as initial values for a future update.

Author(s)

Statisticat, LLC. software@bayesian-inference.com

See Also

Combine, IterativeQuadrature, LaplaceApproximation, LaplacesDemon, LaplacesDemon.hpc, PMC, and VariationalBayes.

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