normal-distribution

问题 I need to generate random numbers that follow a normal distribution which should lie within the interval of 1000 and 11000 with a mean of 7000. I want to use the c++11 library function but I am not understanding how to generate the numbers within the interval. Can someone help? 回答1: You don't specify the standard deviation. Assuming a standard deviation of 2000 for the given interval you can try this: #include <iostream> #include <random> class Generator { std::default_random_engine generator

问题 Situation: Lets consider a massive retail network (hundreds of products and thousands of stores) simplified as follows: Store 1, Store 2 Product A, Product B, Product C I am trying to identify anomalies in sales numbers to know which stores do very well and which do very badly. My first idea was to calculate the means and standard deviations of sales and qualify as anomalies everything that is outside the bounds of 3 standard deviations (~5% of the cases in a normal distribution). However,

问题 The problem I am looking for high precision values for the normal distribution in the tail (1e-10 and 1 - 1e-10) , as the R package that I am using sets any number which is out of this range to these values and then calls the qnorm and qt function. What I have noticed is that the qnorm implementation in R is not symmetric when looking at the tails. This is quite surprising to me, as it is well known that this distribution is symmetric, and I have seen implementations in other languages that

问题 While plotting normal distribution graph of data, how can we put labels like in image below for percentage of data in each bin where each band has a width of 1 standard deviation using matplotlib/seaborn or plotly ? Currently, im plotting like this: hmean = np.mean(data) hstd = np.std(data) pdf = stats.norm.pdf(data, hmean, hstd) plt.plot(data, pdf) 回答1: Although I've labelled the percentages between the quartiles, this bit of code may be helpful to do the same for the standard deviations.

问题 While plotting normal distribution graph of data, how can we put labels like in image below for percentage of data in each bin where each band has a width of 1 standard deviation using matplotlib/seaborn or plotly ? Currently, im plotting like this: hmean = np.mean(data) hstd = np.std(data) pdf = stats.norm.pdf(data, hmean, hstd) plt.plot(data, pdf) 回答1: Although I've labelled the percentages between the quartiles, this bit of code may be helpful to do the same for the standard deviations.

问题 I have 100 sampled numbers, and I need to draw the normal distribution curve of them in matlab. The mean and standard deviation of these sampled data can be calculated easily, but is there any function that plots the normal distribution? 回答1: If you have access to Statistics Toolbox, the function histfit does what I think you need: >> x = randn(10000,1); >> histfit(x) Just like with the hist command, you can also specify the number of bins, and you can also specify which distribution is used

问题 I'm using the following code to fit the normal distribution. The link for the dataset for "b" (too large to post directly) is : link for b setwd("xxxxxx") library(fitdistrplus) require(MASS) tazur <-read.csv("b", header= TRUE, sep=",") claims<-tazur$b a<-log(claims) plot(hist(a)) After plotting the histogram, it seems a normal distribution should fit well. f1n <- fitdistr(claims,"normal") summary(f1n) #Length Class Mode #estimate 2 -none- numeric #sd 2 -none- numeric #vcov 4 -none- numeric #n

问题 Matlab has the function randn to draw from a normal distribution e.g. x = 0.5 + 0.1*randn() draws a pseudorandom number from a normal distribution of mean 0.5 and standard deviation 0.1. Given this, is the following Matlab code equivalent to sampling from a normal distribution truncated at 0 at 1? while x <=0 || x > 1 x = 0.5 + 0.1*randn(); end 回答1: Using MATLAB's Probability Distribution Objects makes sampling from truncated distributions very easy. You can use the makedist and truncate

问题 How do I calculate the inverse of the cumulative distribution function (CDF) of the normal distribution in Python? Which library should I use? Possibly scipy? 回答1: NORMSINV (mentioned in a comment) is the inverse of the CDF of the standard normal distribution. Using scipy , you can compute this with the ppf method of the scipy.stats.norm object. The acronym ppf stands for percent point function, which is another name for the quantile function. In [20]: from scipy.stats import norm In [21]:

问题 I have the represented independently these two plots using R: #PLOT 1 x<-250:2500 #Hsap. Northern European a<-dnorm(x,1489,167) #Hsap. South African b<-dnorm(x,1472,142) plot(x,a, type="l", lwd=3, ylim=c(0,1.2*max(a,b,c)), ylab="Probability Density", xlab="Microns") lines(x,b, type="l", lwd=3, col="Red") PLOT 2 #CUSPAL ENAMEL FORMATION TIME x<-0:800 #Hsap. Northern European a<-dnorm(x,447,37) #Hsap. South African b<-dnorm(x,444,33) plot(x,a, type="l", lwd=3, ylim=c(0,1.2*max(a,b,c)), ylab=