Obtaining samples of an RV with a given CDF

[chroot]

Hey guys,

I know the cdf of a random process, and I want to obtain samples of it computationally. (In MATLAB, specifically.) How can I go about doing this?

The naive approach I took was to put the cdf into a variable, and then use interpolation of the inverse function. More specifically, say the distribution was this:

angles = [ 0 10 20 30 40 50 60 70 80 90 ];
cdf = [ 0 0.3 0.4 0.6 0.8 0.88 0.92 0.94 0.99 1.0 ];

To find a sample of this random process, I do something like this:

angle = interp1(cdf, angles, rand, 'spline');

This seems to work reasonably well, but it has some flaws.

First, it's pretty slow, and is a bottleneck in my program. Yes, linear interpolation is a little faster, but still much slower than what I'd like. I tried a quick 'qinterp1' function I found at the MATLAB file exchange, but could not get it to behave correctly.

Second, it will not work if the cdf is ever horizontal. In other words, the cdf [ 0 0.5 1.0 1.0 ... ] won't work, because interp1 can only work when the x values are distinct.

Is there some better way to do this? I remember seeing a mathematical explanation of how to get samples of a random process with a known cdf in a statistics class, but I suspect it was different than what I'm doing. If I'm doing it the right way conceptually, is there a way to speed up the computation? I have to do this many billions of times, so even just doing some precomputation would be enormously beneficial.

- Warren

 

[bpet]

If you're simulating from a discrete distribution then there are other specialized routines on the file exchange that might help.

Also if the distributions don't change at every step then you could vectorize to reduce overhead, e.g. with "rand(1e6,1)" instead of "rand".

 

[chroot]

Actually, the cdf is continuous, but is not described analytically. Instead I took samples of the cdf.

- Warren

 

[mXSCNT]

Pick a random number, r, uniformly from (0,1) and then perform a binary search in your CDF array for the highest index whose entry is <= r.

 

[bpet]

Actually, the cdf is continuous, but is not described analytically. Instead I took samples of the cdf.

Ok. Looks you're using equally spaced x points. Instead try choosing the x values so that the cdf points are more equally spaced (more work, but this is only done in the preprocessing). At the places where the cdf is horizontal choose the single x value you think is most appropriate, and add more cdf points nearby to make the spline more accurate.

Also with each call of interp1 there would be a lot of overhead for computing the spline. You might be able to speed things up significantly by precomputing it as described in the documentation for interp1.

In general cdf/quantile interpolation can be problematic though, and extra care should be taken with the endpoints especially if the tails are infinite.

Hope this helps.