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"Gonzalo " <glpita@gmail.com> wrote in message <hu26a0$hpf$1@fred.mathworks.com>... > Mr Stafford, > > Yes, I need f(x) to be the pdf for my generated random variable. Now, maybe I'm confused here, but I don't have that freedom to set p4=p5. Those coefficients come from a nonlinear regression of data, and f(x) fits the data very well. Regarding p1, it is approx equal to = 1/(2*(p3-p2)) * 100. Here     

Group: comp.softsys.matlab · Group Profile · Search for Random Variable in comp.softsys.matlab
Author: Roger Stafford
Date: Jun 1, 2010 02:27

"Roger Stafford" <ellieandrogerxyzzy@mindspring.com.invalid> wrote in message <hu22nj$jv4$1@fred.mathworks.com>... "Gonzalo " <glpita@gmail.com> wrote in message <hu1o0g$44h$1@fred.mathworks.com>... ...... I got the integral of f(x) and normalized the function so that Int(f(x)) = 1; the 2nd condition is also met thanks to the type of function chosen. Now, could please explain a little
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"Gonzalo " <glpita@gmail.com> wrote in message <hu1o0g$44h$1@fred.mathworks.com>... > ...... > I got the integral of f(x) and normalized the function so that Int(f(x)) = 1; the 2nd condition is also met thanks to the type of function chosen. Now, could please explain a little bit more on how to use fzero to generate the random numbers from f(x)?? > ...... Gonzalo, you still haven't stated     

Group: comp.softsys.matlab · Group Profile · Search for Random Variable in comp.softsys.matlab
Author: Gonzalo
Date: May 31, 2010 22:37

Thanks all for the inputs! It seems to me now 1. If both x and y are deterministic signal and periodic, orthogonality is the integration of x*y from 0 to T while correlation is the integration of x*y from 0 to n*T. The only difference between orthogonality and correlation is the integration length. 2. If both x and y are random variable, there is no orthogonality as T cannot be defined but correlation
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On May 13, 7:44 am, mike-121 <mike-...@live.co.uk> wrote: > > I need to find the following 1 dimensional integral (hoping for an analytic form or maybe upper and lower bounds which are tight). > > Integral { -inf to inf } Phi^n (x) phi (x-mu) dx > > That is the cdf of a standard normal raised to n, integrated wrt a normal with mean mu, variance 1. > So phi(.) is standard Gaussian pdf and Phi     

Group: comp.softsys.matlab · Group Profile · Search for Random Variable in comp.softsys.matlab
Author: Roger Stafford
Date: May 31, 2010 21:36

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Group: comp.dsp · Group Profile · Search for Random Variable in comp.dsp
Author: cfy30
Date: May 31, 2010 20:23

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Group: sci.stat.math · Group Profile · Search for Random Variable in sci.stat.math
Author: dvsarwate
Date: May 13, 2010 16:24

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