probability-distribution

问题 I am working on a data set consisting of 'pulse length' values and 5 or 6 'voltage values' corresponding to each of the pulse lengths. The first value is pulse length followed by voltages. Find the table below. 15 -56V -47V -53V -50V -50V 16 -49V -46V -52V -47V -50V 17 -50V -51V -47V -50V -49V 18 -50V -51V -48V -48V -45V 19 -49V -51V -45V -47V -52V 20 -45V -47V -50V -47V -54V 21 -46V -52V -52V -49V -54V 22 -53V -51V -53V -56V -52V 23 -52V -45V -51V -56V -53V 24 -51V -52V -54V -58V -52V 25

问题 This is my code and the result I got. Something is not right — I used histfit(cntH,NumBins,'kernel') , and I was expecting the distribution line to start from zero and fit into the bars How can I fix that? clear all clc % yG = total load yH % specify number of bins and edges of those bins; this example evenly spaces bins NumBins = 100; BinEdges = linspace(0,35,70); % use histcounts and specify your bins cntH = histcounts(yH,'BinEdges',BinEdges); % plot figure(1); cla; hold on; % convert bin

问题 Each continuous distribution in scipy.stats comes with an attribute that calculates its differential entropy: .entropy . Unlike the normal distribution ( norm ) and others that have a closed-form solution for entropy, other distributions have to rely on numerical integration. Trying to find out which function the .entropy attribute is calling in those cases, I found a function called _entropy in scipy.stats._distn_infrastructure.py that does so with integrate.quad(pdf) (numerical integration)

问题 The code below maps the statistical moments (mean, variance, skewness, excess kurtosis) generated by corresponding parameters ( a , b , loc , scale ) of the Johnson-SU distribution ( johnsonsu ). For the range of loop values specified in my code below, no parameter configuration results in positive skewness, only negative skewness, even though it should be possible to parameterize the Johnson-SU distribution to be positively-skewed. import numpy as np import pandas as pd from scipy.stats

问题 I am given the following problem to solve (This text is translated from Russian. So, there may be some translation issues): ... Another method to draw from the normal distribution is to draw two independent random numbers from the uniform distribution x1, x2 ∈ [0:0, 1:0). Then apply the following transformation: resulting in two randomly independent numbers n 1 , n 2 from a normal distribution with zero expected value and unit variance. To change the distribution parameters to other

问题 I am given the following problem to solve (This text is translated from Russian. So, there may be some translation issues): ... Another method to draw from the normal distribution is to draw two independent random numbers from the uniform distribution x1, x2 ∈ [0:0, 1:0). Then apply the following transformation: resulting in two randomly independent numbers n 1 , n 2 from a normal distribution with zero expected value and unit variance. To change the distribution parameters to other

问题 I am given the following problem to solve (This text is translated from Russian. So, there may be some translation issues): ... Another method to draw from the normal distribution is to draw two independent random numbers from the uniform distribution x1, x2 ∈ [0:0, 1:0). Then apply the following transformation: resulting in two randomly independent numbers n 1 , n 2 from a normal distribution with zero expected value and unit variance. To change the distribution parameters to other

问题 When I draw displot for discrete variables, the distribution might not be as what I think. For example. We can find that there are crevices in the barplot so that the curve in kdeplot is "lower" in y axis. In my work, it was even worse: I think it may because the "width" or "weight" was not 1 for each bar. But I didn't find any parameter that can justify it. I'd like to draw such curve (It should be more smooth) 回答1: If the problem is that there are some emptry bins in the histogram, it

问题 When I draw displot for discrete variables, the distribution might not be as what I think. For example. We can find that there are crevices in the barplot so that the curve in kdeplot is "lower" in y axis. In my work, it was even worse: I think it may because the "width" or "weight" was not 1 for each bar. But I didn't find any parameter that can justify it. I'd like to draw such curve (It should be more smooth) 回答1: If the problem is that there are some emptry bins in the histogram, it

问题 When I draw displot for discrete variables, the distribution might not be as what I think. For example. We can find that there are crevices in the barplot so that the curve in kdeplot is "lower" in y axis. In my work, it was even worse: I think it may because the "width" or "weight" was not 1 for each bar. But I didn't find any parameter that can justify it. I'd like to draw such curve (It should be more smooth) 回答1: If the problem is that there are some emptry bins in the histogram, it