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Multitaper
- 工程计算MATLAB code to calculate the reorthogonalized sine tapers input: N = the length of the time series data to be tapered p = the number of tapers requested I = the gap structure a vector of length N I(t) = 1 if there is data at time
fit_maxwell_pdf
- fit_maxwell_pdf - Non Linear Least Squares fit of the maxwellian distribution. given the samples of the histogram of the samples, finds the distribution parameter that fits the histogram samples. fits data to the probability of the form:
fit_ML_laplace
- fit_ML_normal - Maximum Likelihood fit of the laplace distribution of i.i.d. samples!. Given the samples of a laplace distribution, the PDF parameter is found fits data to the probability of the form: p(x) = 1/(2*b)*exp(-abs(x-u)/b)
fit_ML_log_normal
- fit_ML_normal - Maximum Likelihood fit of the laplace distribution of i.i.d. samples!. Given the samples of a laplace distribution, the PDF parameter is found fits data to the probability of the form: p(x) = 1/(2*b)*exp(-abs(x-u)/b)
fit_ML_maxwell
- fit_ML_normal - Maximum Likelihood fit of the log-normal distribution of i.i.d. samples!. Given the samples of a log-normal distribution, the PDF parameter is found fits data to the probability of the form: p(x) = sqrt(1/(2*pi))/(s*x)*
fit_ML_normal
- fit_ML_normal - Maximum Likelihood fit of the normal distribution of i.i.d. samples!. Given the samples of a normal distribution, the PDF parameter is found fits data to the probability of the form: p(r) = sqrt(1/2/pi/sig^2)*exp(-((r-u
fit_ML_rayleigh
- fit_ML_rayleigh - Maximum Likelihood fit of the rayleigh distribution of i.i.d. samples!. Given the samples of a rayleigh distribution, the PDF parameter is found fits data to the probability of the form: p(r)=r*exp(-r^2/(2*s))/s wit
fit_rayleigh_pdf
- fit_rayleigh_pdf - Non Linear Least Squares fit of the Rayleigh distribution. given the samples of the histogram of the samples, finds the distribution parameter that fits the histogram samples.fits data to the probability of the form: p(r)=r*exp(-