1. one option is built in randsample with weighted option, you sample with certain weights.
There are CFX options
2. Discrete (multinomial) distribution sampler
http://www.mathworks.com/matlabcentral/fileexchange/25481-discrete-multinomial-distribution-sampler
3. randp
http://www.mathworks.com/matlabcentral/fileexchange/8891
Tuesday, August 2, 2011
Matlab Simulation: Weighted Without Replacement Sampling
1. matlab's randsample doesn't handle this
Y = RANDSAMPLE(...,true,W) returns a weighted sample, using positive
weights W, taken with replacement. W is often a vector of probabilities.
This function does not support weighted sampling without replacement.
2. Someone on CFX wrote a file for this
http://www.mathworks.com/matlabcentral/fileexchange/27263-modified-randsample
Returns V, a weigthed sample of K elements taken among X without replacement
X a vector of numerics
K amount of element to sample from x
W a vector of positive weights w, whose length is length(x)
3. I think this will take care of multivariate hypergeometric sampling. That is sampling without replacement when there are multiple types, because the different types each have a different probability of being selectted.
Y = RANDSAMPLE(...,true,W) returns a weighted sample, using positive
weights W, taken with replacement. W is often a vector of probabilities.
This function does not support weighted sampling without replacement.
2. Someone on CFX wrote a file for this
http://www.mathworks.com/matlabcentral/fileexchange/27263-modified-randsample
Returns V, a weigthed sample of K elements taken among X without replacement
X a vector of numerics
K amount of element to sample from x
W a vector of positive weights w, whose length is length(x)
3. I think this will take care of multivariate hypergeometric sampling. That is sampling without replacement when there are multiple types, because the different types each have a different probability of being selectted.
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