expectation.lgcpPredict: expectation.lgcpPredict function

Description

This function requires data to have been dumped to disk: see ?dump2dir and ?setoutput. This function computes the Monte Carlo Average of a function where data from a run of lgcpPredict has been dumped to disk.

Usage

# S3 method for lgcpPredict
expectation(obj, fun, maxit = NULL, ...)

Arguments

obj

an object of class lgcpPredict

fun

a function accepting a single argument that returns a numeric vector, matrix or array object

maxit

Not used in ordinary circumstances. Defines subset of samples over which to compute expectation. Expectation is computed using information from iterations 1:maxit, where 1 is the first non-burn in iteration dumped to disk.

...

additional arguments

Value

the expectated value of that function

Details

A Monte Carlo Average is computed as: $E_{π (Y_{t_{1} : t_{2}} | X_{t_{1} : t_{2}})} [g (Y_{t_{1} : t_{2}})] \approx \frac{1}{n} \sum_{i = 1}^{n} g (Y_{t_{1} : t_{2}}^{(i)})$ where $g$ is a function of interest, $Y_{t_{1} : t_{2}}^{(i)}$ is the $i$ th retained sample from the target and $n$ is the total number of retained iterations. For example, to compute the mean of $Y_{t_{1} : t_{2}}$ set, $g (Y_{t_{1} : t_{2}}) = Y_{t_{1} : t_{2}},$ the output from such a Monte Carlo average would be a set of $t_{2} - t_{1}$ grids, each cell of which being equal to the mean over all retained iterations of the algorithm (NOTE: this is just an example computation, in practice, there is no need to compute the mean on line explicitly, as this is already done by default in lgcpPredict).

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