low_var_sample: Sample particles using low-variance sampling.

In animalEKF: Extended Kalman Filters for Animal Movement

Sample particles using low-variance sampling.

Description

Sample particles using low-variance sampling.

Usage

low_var_sample(wts, M=length(wts))

Arguments

wts	Vector of weights.
M	The number of items to sample. When resampling, should be number of particles npart.

Details

Low-variance sampling guarantees items will be sampled in proportion to their weights. With random sampling with replacement (sample function), there is some variability in the final proportions of items.

Value

A numeric vector of length M.

Author(s)

Samuel Ackerman

References

James Edward Baker. Reducing bias and inefficiency in the selection algorithm. Proceedings of the Second International Conference on Genetic Algorithms on Genetic Algorithms and Their Application, 1987.

Examples

n <- 20
w <- runif(n)

#can use M != to length(wts)
low_var_sample(wts=w, M=15)

lv <- low_var_sample(wts=w, M=n)

#usual resampling with weights
a <- sample(x=1:n, size=n, prob=w, replace=TRUE)

#the first should be more in proportion to weights
table(lv)/n
table(a)/n
#proportions
w/sum(w)

animalEKF documentation built on Sept. 29, 2023, 5:08 p.m.