View source: R/low_var_sample.r

low_var_sample | R Documentation |

Sample particles using low-variance sampling.

```
low_var_sample(wts, M=length(wts))
```

`wts` |
Vector of weights. |

`M` |
The number of items to sample. When resampling, should be number of particles |

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.

A numeric vector of length `M`

.

Samuel Ackerman

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.

```
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)
```

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