I have my own function. I need to generate its 10000 values which should be random like the rayleigh fading values generated using h=randn(N,1)+i*rand(N,1) like this line generate N random values similarly I need now similar values of my distribution. my function has input of three arguments whose values are different in one scenario . what I understand that I can take fix three values for one time and evaluate my function then get it one value. output values of my function is of complex values. whether I need to compute variance and average of my function values then loop over in between these values to get 10000 values .write in an array form .please correct me whether this is right way or what should I do.how do I write code in Matlab.?
Usually, you would take a random number generator (RNG) that can provide uniformly distributed values between 0 and 1. Here's how the mapping is generally done: First, from your probability distribution function (PDF) you generate the cumulative distribution function (CDF) through integration over x. The CDF will always be invertible. Now, you can pick any random number from a uniform distribution and look up the x-value of your function through the inverse CDF. This x-value will be a random number from your PDF. Sometimes the CDF cannot be determined by a formula, so that you'll have to find a numerical approach to this. With the right keywords you should be able to find papers on this.
Here's a link on what Wikipedia has to say on this:
http://en.wikipedia.org/wiki/Inverse_transform_sampling
Thanks Erwin .my function is not rayleigh, it is given as,
f|hi|2 (x) = αie−βix1F1(mi; 1; δix), x>0, where αi,βi,δi are given as αi = 0.5(2bimi/(2bimi + Ωi))mi/bi,
βi = (0.5/bi),
δi = 0.5Ωi/(2b2i mi +biΩi),
these alphas,betas, and deltas are fixed for one iteration.
please help how can i define and get its 10000 random values for this probability density function.any help regards
Emm, Shakeel, I don't quite get it though. What is the purpose of generating 10k random values? Are you using it for parameter identification or what?
Plus, what are the requirements for the random values... the function randn() is able to generate a series of normally distributed numbers. If you want the numbers to be within (0,1), normally what I do is put sin or cos to the randn function. Such as " sin(randn(N,1)). So for a N-element vector, you have range within (0,1).
Hope that helps.
Best Regards,
Kurt
Usually, you would take a random number generator (RNG) that can provide uniformly distributed values between 0 and 1. Here's how the mapping is generally done: First, from your probability distribution function (PDF) you generate the cumulative distribution function (CDF) through integration over x. The CDF will always be invertible. Now, you can pick any random number from a uniform distribution and look up the x-value of your function through the inverse CDF. This x-value will be a random number from your PDF. Sometimes the CDF cannot be determined by a formula, so that you'll have to find a numerical approach to this. With the right keywords you should be able to find papers on this.
Here's a link on what Wikipedia has to say on this:
http://en.wikipedia.org/wiki/Inverse_transform_sampling
i am facing problem to generate random numbers from my own distribution which i generate through generator.
Please tell me how to generate?
Simon Schröder has the right and simplest way for this. Here is a simple python code to achieve this
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