Generate Random Sample of 0's and 1's

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Joe am 16 Jan. 2014

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https://de.mathworks.com/matlabcentral/answers/112654-generate-random-sample-of-0-s-and-1-s

Kommentiert: Suneesh am 16 Jan. 2014

Hi guys,

how do I generate a random sample of 0's and 1's of size N, such that the probability of of 1 being chosen is p.

I can create a permutation however I can't seem to incorporate probability p into it. Plus, also is there a way I could quickly sum the components of the permutation.

Thanks,

Joe

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Answer 1

Suneesh am 16 Jan. 2014

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Direkter Link zu dieser Antwort

https://de.mathworks.com/matlabcentral/answers/112654-generate-random-sample-of-0-s-and-1-s#answer_121124

Bearbeitet: Suneesh am 16 Jan. 2014

In MATLAB Online öffnen

MATLAB has a function RANDSRC (part of the Comm Sys Tbx) which does this.

out = randsrc(1,N,[0 1; (1-p) p])

See documentation for the function to learn more about the usage.

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Joe am 16 Jan. 2014

thanks this is useful, how about summing the individual components, is there a function?

Suneesh am 16 Jan. 2014

The SUM function

http://www.mathworks.com/help/matlab/ref/sum.html

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Answer 2

Rajiv Ghosh-Roy am 16 Jan. 2014

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https://de.mathworks.com/matlabcentral/answers/112654-generate-random-sample-of-0-s-and-1-s#answer_121121

In MATLAB Online öffnen

How about

a = rand(1, N) < p

This creates a random vector of size N, and the < will return 1 if true, 0 if false.

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Rajiv Ghosh-Roy am 16 Jan. 2014

Please consult the help for rand. rand(1, N) will generate a 1xN vector of random numbers uniformly distributed in [0, 1]. The probability that a number is in (0, p) is then p.

Suneesh am 16 Jan. 2014

Bearbeitet: Suneesh am 16 Jan. 2014

Joe, your problem may be stated as "Generate random numbers that have a Bernoulli distribution with the head(1) probability = p". There are several algorithms for generating random numbers and many start with generating random numbers that are uniformly distributed. This is what RAND does. The comparison " < p" owes to the inverse transform method of generating arbitrarily distributed random numbers from a uniform distribution. However, as far as I remember it should be "<=p". This method is standard in several text books on beginning probability theory. See also:

1. http://www.mathworks.com/help/stats/common-generation-methods.html

2. "Inverse Transform Method for simulating continuous random variables." in

http://www.cs.bgu.ac.il/~mps042/invtransnote.htm

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