Scriptcase pk showing up as global variable pdf#
pdf ( xs ), 'r-', label = "True PDF" ) ax1. plot ( xs, kde2 ( xs ), 'b-', label = "Silverman's Rule" ) ax1. plot ( xs, kde1 ( xs ), 'k-', label = "Scott's Rule" ) ax1. shape ), 'b+', ms = 12 ) # rug plot ax1. gaussian_kde ( x1, bw_method = 'silverman' ) fig = plt. normal ( size = 200 ) # random data, normal distribution xs = np. Import numpy as np import matplotlib.pyplot as plt from scipy import stats rng = np. Of the distribution, and the test is repeated using probabilities of the The fit method of the distributions can be used to estimate the parameters The estimate for scale and location into account. Since the variance of our sampleĭiffers from both standard distributions, we can again redo the test taking Standard t-distribution cannot be rejected. We see that the standard normal distribution is clearly rejected, while the
chisquare ( freqcount, nprob * n_sample ) > print ( 'chisquare for t: chi2 = %6.2f pvalue = %6.4f ' % ( tch, tpval )) chisquare for t: chi2 = 2.30 pvalue = 0.8901 # random > print ( 'chisquare for normal: chi2 = %6.2f pvalue = %6.4f ' % ( nch, npval )) chisquare for normal: chi2 = 64.60 pvalue = 0.0000 # random chisquare ( freqcount, tprob * n_sample ) > nch, npval = stats. histogram ( x, bins = crit ) > tprob = np. ppf ( quantiles, 10 ) > crit array() > n_sample = x. histogram ( rvs, bins = gridlimits ) > sfreq = np. Random variables on my computer, while one million random variablesįrom the standard normal or from the t distribution take just above Variables in a very indirect way and takes about 19 seconds for 100 As an example, rgh = (0.5, 2, 2, 2, size=100) creates random Using numeric integration and root finding. Only one of pdf or cdf is necessary all other methods can be derived The generic methods, on the other hand, are used if the distributionĭoes not specify any explicit calculation. Generic algorithm that is independent of the specific distribution.Įxplicit calculation, on the one hand, requires that the method isĭirectly specified for the given distribution, either through analyticįormulas or through special functions in scipy.special or
Obtained in one of two ways: either by explicit calculation, or by a The performance of the individual methods, in terms of speed, varies Performance issues and cautionary remarks ¶
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