probability-density

问题 Following my previous question titled: "Random sampling from a dataset, while preserving original probability distribution", I want to sample from a set of >2000 numbers, gathered from measurement. I want to perform several tests (I take maximum of 10 samples in each tests), while preserving probability distribution in overall testiong process, and in each test (as much as possible). Now, instead of completely random sampling, I partition data into 5 quantiles, and in 10 tests, I sample 2

问题 I am using the scipy.stats.gaussian_kde method from scipy to generate random samples from the data. It works fine! What I have now found out is that the method also has inbuilt functions to calculate the probability density function of the given set of points (my data). I would like to know how it calculates the pdf provided a set of points. Here is small example: import numpy as np import scipy.stats from scipy import stats def getDistribution1(data): kernel = stats.gaussian_kde(data,bw

问题 I'm trying to sort a set of data so that it looks like a histogram of a probability distribution function (I'm assuming normally distributed for the moment). I have a list of entries: private static final class SortableDatasetEntry{ Number value; Comparable key; public SortableDatasetEntry(Number value, Comparable key){ this.value = value; this.key = key; } } An example: I have the items : {1,2,3,4,5,6,7,8,9} EDIT: The sorted list I would like: {1,3,5,7,9,8,6,4,2} (or something similar) The

问题 I am using Gaussian kernel to estimate a pdf of a data based on the equation where K(.) is Gaussian kernel, data is a given vector. z is bin from 1 to 256. size of bin is 1. I implemented by matlab code. However, the result show the amplitude of my pdf estimation (blue color) is not similar with real pdf of data. Could you see my code and give me some comment about my code? MATLAB CODE function pdf_est=KDE() close all; %%Random values of 20 pixels, range=[1 256] data=randi([1 256],1,20); %%

问题 I want to plot some points with the rbf function like here to get the density distribution of the points: if i run the following code, it works fine: from scipy.interpolate.rbf import Rbf # radial basis functions import cv2 import matplotlib.pyplot as plt import numpy as np # import data x = [1, 1, 2 ,3, 2, 7, 8, 6, 6, 7, 6.5, 7.5, 9, 8, 9, 8.5] y = [0, 2, 5, 6, 1, 2, 9, 2, 3, 3, 2.5, 2, 8, 8, 9, 8.5] d = np.ones(len(x)) print(d) ti = np.linspace(-1,10) xx, yy = np.meshgrid(ti, ti) rbf = Rbf

问题 I know this is going to end up being a really messy plot, but I am curious to know what the most efficient way to do this is. I have some data that looks like this in a csv file: ROI Band Min Max Mean Stdev 1 red_2 Band 1 0.032262 0.124425 0.078073 0.028031 2 red_2 Band 2 0.021072 0.064156 0.037923 0.012178 3 red_2 Band 3 0.013404 0.066043 0.036316 0.014787 4 red_2 Band 4 0.005162 0.055781 0.015526 0.013255 5 red_3 Band 1 0.037488 0.10783 0.057892 0.018964 6 red_3 Band 2 0.02814 0.07237 0

问题 This question already has answers here : Find the probability density of a new data point using “density” function in R (3 answers) Density Value for each Return (3 answers) Closed 2 years ago . I was wondering if there is a R base way to obtain the exact kernel density at any point desired? As an example, how can I get the exact kernel density at the 3 following points -2, 0, +2 on X-Axis in a plot like below? set.seed(2937107) plot( density(rnorm(1e4)) ) 回答1: Use linear interpolation to

问题 I have a dataset with 3-hourly precipitation amounts for the month of January in the period 1977-1983 (see attachment). However, I want to generate precipitation data for the period 1984-1990 based upon these data. Therefore, I was wondering if it would be possible to create a custom made probability density function of the precipitation amounts (1977-1983) and from this, generate random numbers (precipitation data) for the desired period (1984-1990). Is this possible in Matlab and could

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 9 months ago . I have a large data set that contains 3 attributes per row: A,B,C Column A: can take the values 1, 2, and 0. Column B and C: can take any values. I'd like to perform density estimation using histograms for P(A = 2 | B,C) and plot the results using python. I do not need the code to do it, I can try and figure

问题 I am struggling to plot the PDF and CDF graphs of where Sn=X1+X2+X3+....+Xn using central limit theorem where n = 1; 2; 3; 4; 5; 10; 20; 40 I am taking Xi to be a uniform continuous random variable for values between (0,3). Here is what i have done so far - close all %different sizes of input X %N=[1 5 10 50]; N = [1 2 3 4 5 10 20 40]; %interval (1,6) for random variables a=0; b=3; %to store sum of differnet sizes of input for i=1:length(N) %generates uniform random numbers in the interval X