expectation.lgcpPredict: expectation.lgcpPredict function
In bentaylor1/lgcp: Log-Gaussian Cox Process

Description Usage Arguments Details Value See Also

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.

1 2	## S3 method for class 'lgcpPredict' expectation(obj, fun, maxit = NULL, ...)

`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

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 \frac1n∑_{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 ith 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).

the expectated value of that function

lgcpPredict, dump2dir, setoutput

bentaylor1/lgcp documentation built on May 12, 2019, 2:09 p.m.