statistics

问题 from random import randint numberOfDoors = 3 success = 0 attempts = 0 while True: try: doors = [0] * numberOfDoors doors[randint(0, numberOfDoors - 1)] = 1 chosen = randint(0, numberOfDoors - 1) while numberOfDoors > 2: notIn = -1 while notIn == -1: index = randint(0, numberOfDoors - 1) if doors[index] == 0 and index != chosen: notIn = index if notIn < chosen: chosen -= 1 del doors[notIn] numberOfDoors -= 1 # doors is 2, so not chosen (0 or 1) will return the opposite (1 or 0) success +=

问题 In the following code, the density=True returns probability density function at each bin. Now if have to calculate P(x), can I say that hist is showing probabilities? For example if the first bin's mean value is 0.5 can I say that at x=0.5 probability is hist[0] ? I have to use KL divergence which uses P(x). x = np.array([0,0,0,0,0,3,3,2,2,2,1,1,1,1,]) hist,bin_edges= np.histogram(x,bins=10,density=True) 回答1: When you set density=True , NumPy returns a probability density function (lets say p

问题 metafor::forest prepares for headings etc by creating a horizontal line and three blank rows in the top of the plot. Is there a way to avoid that? I have too cases where this poses a problem: For a simple forest plot, one header row is sufficient. I have to manually add a title just above the horizontal line using text rather than title and then crop the image afterwards. I want to create a forest plot of pure summary estimates using addpoly . I have to crop the top of the image because of

问题 I have a dataset containing two variables and I wish to statistically test whether they are related in a bootstrap loop (i.e. using Spearman’s rank correction with cor.test(...) ). Most of the measurements in my dataset are from independent sample units (let’s call the units plants), although some measurements come from the same plant. To deal with issues of pseudoreplication, I wish to bootstrap the statistic test a number of times, using only one measurement from each plant in each run of

问题 I have read a image file into a array like this A = imread(fileName); and now i want to calculate shannon entropy. The shannon entropy implementation found in maltab is a byte level entropy analysis which considers a file to be composed of 256 byte levels. wentropy(x,'shannon') But i need to perform a bigram entropy analysis which would need to view a file as consisting of 65536 levels. Could anyone suggest me a good method of accomplishing this. 回答1: The entropy of a random variable can be

问题 I'm struggling with this now for a few days. This is now the 3rd question at stackoverflow about the same topic, hope this time my question is better defined. My data are distributed like this: (histogram) The x-axis correspond to the range of probabilities: from 0 to 1. I want to assign states from state 1 to state 10 sensibly to the probability range. This is what I have got: Interval <- round(quantile(datag, c(seq(0,1,by=0.10))),3) output: 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0.000

问题 suppose I have the following 2 random variables : X where mean = 6 and stdev = 3.5 Y where mean = -42 and stdev = 5 I would like to create a new random variable Z based on the first two and knowing that : X happens 90% of the time and Y happens 10% of the time. It is easy to calculate the mean for Z : 0.9 * 6 + 0.1 * -42 = 1.2 But is it possible to generate random values for Z in a single function? Of course, I could do something along those lines : if (randIntBetween(1,10) > 1)

问题 I have a set S={a1,a2,a3,a4,a5,......,an}. The probability with which each of the element is selected is {p1,p2,p3,p4,p5,...,pn} respectively (where ofcourse p1+p2+p3+p4+p5+....+pn=1}. I want to simulate an experiment which does that. However I wish to do that without any libraries (i.e from first principles) I'm using the following method: 1) I map the elements on the real number line as follows X(a1)=1; X(a2)=2; X(a3)=3; X(a4)=4; X(a5)=5;....,X(an)=n 2) Then I calculate the cumulative

问题 Folks,i have been trying to obtain a Gaussian fit for some data sets which somehow look like a distorted normal distribution.I have been using software to do that. I wonder if i can apply an iterative algorithm to convert these data sets to a Gaussian fitted curve,the standard deviation and mean of the original curve being the inputs.? Any ideas? 回答1: Calculate the mean of the data: mu = 1/N Sum(xi) Calculate the dispersion of the data: sigma = sqrt(1/(N-1) Sum(xi-mu)) Fill in the parameters:

问题 I am looking for a math equation or algorithm which can generate uniform random numbers in ascending order in the range [0,1] without the help of division operator. i am keen in skipping the division operation because i am implementing it in hardware. Thank you. 回答1: Generating the numbers in ascending (or descending) order means generating them sequentially but with the right distribution. That, in turn, means we need to know the distribution of the minimum of a set of size N, and then at