distribution

问题 This question already has answers here : How do I best simulate an arbitrary univariate random variate using its probability function? (4 answers) Closed 5 years ago . For a normalized probability density function defined on the real line, for example p(x) = (2/pi) * (1/(exp(x)+exp(-x)) (this is just an example; the solution should apply for any continuous PDF we can define) is there a package in R to simulate from the distribution? I am aware of R's built-in simulators for many distributions

问题 I'm trying to "share" .net objects across separate processes. I have one type of process that's a web service that manipulates a set of domain entities. Another type of process is a window service that's doing some automatic batch processing on the same set of objects. Beyond the typical solution of having the DB as the shared space where both types of processes read/write the objects, what might be a better, more distributed architecture for having these different processes see and work on

问题 I had to update the maven release plugin from version 2.2.1 to 2.2.2 to get around a bug, but now, when doing a maven release, the files for the new version are not uploaded to my distribution management. I have tried the following: mvn release:prepare & mvn release:perform with version 2.2.2 of the release-plugin: Uploads the new SNAPSHOT-version mvn release:prepare with version 2.2.1, mvn release:perform with version 2.2.2: Uploads the new version (which is what i want) So somehow the setup

问题 I had to update the maven release plugin from version 2.2.1 to 2.2.2 to get around a bug, but now, when doing a maven release, the files for the new version are not uploaded to my distribution management. I have tried the following: mvn release:prepare & mvn release:perform with version 2.2.2 of the release-plugin: Uploads the new SNAPSHOT-version mvn release:prepare with version 2.2.1, mvn release:perform with version 2.2.2: Uploads the new version (which is what i want) So somehow the setup

问题 I used the following function to estimate the three-parameter Weibull distribution. library(bbmle) library(FAdist) set.seed(16) xl=rweibull3(50, shape = 1,scale=1, thres = 0) dweib3l <- function(shape, scale, thres) { -sum(dweibull3(xl , shape, scale, thres, log=TRUE)) } ml <- mle2(dweib3l, start= list(shape = 1, scale = 1, thres=0), data=list(xl)) However, when I run the above function I am getting the following error. Error in optim(par = c(shape = 1, scale = 1, thres = 0), fn = function (p

问题 How can I easily compare the distributions of multiple cohorts? Usually, https://seaborn.pydata.org/generated/seaborn.distplot.html would be a great tool to visually compare distributions. However, due to the size of my dataset, I needed to compress it and only keep the counts. It was created as: SELECT age, gender, compress_distributionUDF(collect_list(struct(target_y_n, count, distribution_value))) GROUP BY age, gender where compress_distributionUDF simply takes a list of tuples and returns

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 8 years ago . We have build an app. A company has bought an customized version of that app. We don't have access to the other companies Apple Developer account but the other company wants to distribute the app under their own name. Is there any way we can hand over the app so that the other company can sign and distribute the

问题 From this source, I am trying to reproduce the following plot: Earlier, I posted a question regarding the log10-log10 plot of the lognormal distribution, and got an answer here: # lognormal base log10 pdf, w is in log10 lognorm_base10 <- function(w, mu, sigma) { log10(exp(1)) / (sqrt(2*pi*sigma^2) * 10^w) * exp(- (w - mu)^2 / (2 * sigma^2)) } # Generate data for mu = 0, sigma = 10 x = seq(0, 10, length.out = 101) y = lognorm_base10(x[2:101], 0, 10) I thought overlapping the power law was

问题 I need to reproduce the normalized density p(x) below, but the code given does not generate a normalized PDF. clc, clear % Create three distribution objects with different parameters pd1 = makedist('Uniform','lower',2,'upper',6); pd2 = makedist('Uniform','lower',2,'upper',4); pd3 = makedist('Uniform','lower',5,'upper',6); % Compute the pdfs x = -1:.01:9; pdf1 = pdf(pd1,x); pdf2 = pdf(pd2,x); pdf3 = pdf(pd3,x); % Sum of uniforms pdf = (pdf1 + pdf2 + pdf3); % Plot the pdfs figure; stairs(x,pdf,

问题 I need to reproduce the normalized density p(x) below, but the code given does not generate a normalized PDF. clc, clear % Create three distribution objects with different parameters pd1 = makedist('Uniform','lower',2,'upper',6); pd2 = makedist('Uniform','lower',2,'upper',4); pd3 = makedist('Uniform','lower',5,'upper',6); % Compute the pdfs x = -1:.01:9; pdf1 = pdf(pd1,x); pdf2 = pdf(pd2,x); pdf3 = pdf(pd3,x); % Sum of uniforms pdf = (pdf1 + pdf2 + pdf3); % Plot the pdfs figure; stairs(x,pdf,