I'm new to matlab. I want to generate noise samples of Gaussian mixture with PDF= sqrt((u.^3)./pi).*exp(-u.*(x.^2)) + sqrt((1-u)^3/pi).*exp(-(1-u).*x.^2);
The length of noise sample is (1,200) Please help me out.
noise samplessignal processing
syms x zint(exp(-x)*1/sqrt(2*pi)*exp(-(z/x)^2/2)/x,x,[0,inf])ans =piecewise(angle(1/z^2) in Dom::Interval([-pi/2], [pi/2]), (1125899906842624*meijerG([1/2, 1, 1], [], [], [], 8/z^2))/(5644425081792261*pi^(1/2)), ~angle(1/z^2) in Dom::Interval([-pi/2], [pi/2]), int((2251799813685248*exp(-x)*exp(-z^2/(2*x^2)))/(5644425081792261*x), x, 0, Inf))
x = exprnd(1,1,1e8);y = randn(1,1e8);mean(x.*y)ans = -0.00017924var(x.*y)ans = 2.0007skewness(x.*y)ans = -0.0017322kurtosis(x.*y)ans = 18.055
hist(x.*y,1000)
[r,type] = pearsrnd(0,sqrt(2),0,18,0,0)r = []type = 7
x = exprnd(1,1,1e6);y = randn(1,1e6);z = (x.*y)';D = fitdist(z,'tlocationscale')D = tLocationScaleDistribution t Location-Scale distribution mu = 0.000146401 [-0.000891219, 0.00118402] sigma = 0.407176 [0.40559, 0.408768] nu = 1.22331 [1.21773, 1.22891]histogram(z',100,'normalization','pdf')hold onfplot(@D.pdf)
doc normcdfdoc normpdf
>> lookfor normalrealmin - Smallest positive normalized floating point number.randn - Normally distributed pseudorandom numbers.sprandn - Sparse normally distributed random matrix.surfnorm - Surface normals.isonormals - Isosurface normals.cde - cd elliptic function with normalized complex argument.sne - sn elliptic function with normalized complex argument.addfreqcsmenu - Add a cs menu to switch between linear and normalized frequencyconvertfrequnits - converts between Normalized, Hz, kHz, etchistfit - Histogram with superimposed fitted normal density.jbtest - Jarque-Bera hypothesis test of composite normality.lhsnorm - Generate a latin hypercube sample with a normal distributionlogncdf - Lognormal cumulative distribution function (cdf).lognfit - Parameter estimates and confidence intervals for lognormal data.logninv - Inverse of the lognormal cumulative distribution function (cdf).lognlike - Negative log-likelihood for the lognormal distribution.lognpdf - Lognormal probability density function (pdf).lognrnd - Random arrays from the lognormal distribution.lognstat - Mean and variance for the lognormal distribution.mvncdf - Multivariate normal cumulative distribution function (cdf).mvnpdf - Multivariate normal probability density function (pdf).mvnrnd - Random vectors from the multivariate normal distribution.normcdf - Normal cumulative distribution function (cdf).normfit - Parameter estimates and confidence intervals for normal data.norminv - Inverse of the normal cumulative distribution function (cdf).normlike - Negative log-likelihood for the normal distribution.normpdf - Normal probability density function (pdf).normplot - Displays a normal probability plot.normrnd - Random arrays from the normal distribution.normspec - Plots normal density between specification limits.normstat - Mean and variance for the normal distribution.logn3fit - Fit a 3-param lognormal dist'n using cumulative probabilities.wgtnormfit - Fitting example for a weighted normal distribution.wgtnormfit2 - Fitting example for a weighted normal distribution (log(sigma) parameterization).>>
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