distribution

问题 I've been searching stackoverflow, reading in the various blogs to get answer to my question "Can I distribute my app to someone, without getting his device UDID?" The answers I found is all about "NO! You must have the list of UDIDs" BUT, I've people (private company) telling me that they used to install apps without giving their device UDIDs to developer. Developer used to send them .ipa file, they just drag&drop it in itunes, that's it! I'm dizzy.. By which way they did it without UDIDs?

问题 Question In R, I would like to create n variables of length L which relationship is given by a correlation matrix called cor_matrix . The important point is that the n variables may follow different distributions (including continuous vs discrete distributions). Related posts how-to-generate-sample-data-with-exact-moments generate-a-random-variable-with-a-defined-correlation-to-an-existing-variable r-constructing-correlated-variables Modified from the third post listed above, the following is

问题 Lets say I have a large data set to where I can manipulate it all in some sort analysis. Which can be looking at values in a probability distribution. Now that I have this large data set, I then want to compare known, actual data to it. Primarily, how many of the values in my data set have the same value or property with the known data. For example: This is a cumulative distribution. The continuous lines are from generated data from simulations and the decreasing intensities are just

问题 I am totally aware of billions how to and forum discussions out there about this issue. Because I could not find the solution I'm writing here. Typical situation: I want a co-worker to test my App. I thought on Ad-Hoc distribution. I followed every kind of tutorials. But I receive always a "valid signer" issue in iTunes. Basically: 1) have a distribution certification which works like a charm to upload apps to iTunes; 2) add UUID number of my friend's iPhone in the portal 3) use the same App

问题 Consider the following code: import scipy print(scipy.__version__) # gives 0.19.1 # Scipy.stats.uniform unif = scipy.stats.uniform(1, 2) print(unif.a, unif.b, unif.args) # gives a=0, b=1, args=(1,2) It seems, regardless of the value I provide for loc and scale , the uniform -function returns a=0,b=1 . Compare that to e.g. randint : # Scipy.stats.randint randi = scipy.stats.randint(1, 10) print(randi.a, randi.b, randi.args) # gives a=1, b=9, args=(1,10) ...which returns what I would expect. So

问题 I want to fit some field data with a custom-made negative exponential probability density function. (Motivation- I eventually want to fit the field data to many of the distributions in table 3 of Bullock Shea and Skarpaas 2006). First I defined a dnegexp function according to this post: Error with custom density function definition for mle2 formula call dnegexp <- function(x, mya, myb, log=FALSE){ logne <- log(mya)-myb*x if(log) return(logne) else return(exp(logne)) } Then I made an rnegexp

问题 I've started doing more and more work with the new Mathematica statistics and data analysis features. I attended the "Statistics & Data Analysis with Mathematica" online seminar on Tuesday (great presentation, I highly recommend it) but I've run into some problems that I hope someone on this forum might have a few moments to consider. I've created a rather extensive notebook to streamline my data analysis, call it "AnalysisNotebook". It outputs an extensive series of charts and data including

问题 I want to implement equivalent of matlab icdf function in C++, I have already found this useful post: https://www.johndcook.com/blog/cpp_phi_inverse/. But I want it with optional mu and sigma parameters as in matlab. What I am supposed to change? 回答1: Inspired from https://gist.github.com/kmpm/1211922/6b7fcd0155b23c3dc71e6f4969f2c48785371292: double inverse_of_normal_cdf(const double p, const double mu, const double sigma) { if (p <= 0.0 || p >= 1.0) { std::stringstream os; os << "Invalid

问题 I have been tackling this issue for days now. I am running an evolutionary model which uses a distribution for interactions between species. I pasted the function here. I need to use the template: template <class InputIt> The function does not recognize the template if I paste it directly before the function declaration. If I paste it before main(), the template is recognized, but I get the single error: Error 2 error LNK1120: 1 unresolved externals The code: void evolution(TeamArray&

问题 I want to develop a lognormal distribution with range [0.42,1.19], whose few elements are given as D=[1.19,1.00,0.84,0.71,0.59,0.50,0.42] . The mean should be 0.84 and standard deviation as small as possible. Also given is that the 90% of cdf (=90% of the grains) lies between 0.59 and 1.19 . Once I know all the elements of this lognormal distribution which incorporate the given conditions I can find its pdf, which is what I require. Here are simple steps I tried: D=[1.19,1.00,0.84,0.71,0.59,0