distribution

问题 I am looking for a distribution or rather a function that returns an integer in a specific range with decreasing probability the higher the number. Lets say the range is from 1 to 5. 85% of the time the function should return 1 8% of the time the function should return 2 4% of the time the function should return 3 2% of the time the function should return 4 1% of the time the function should return 5 Additionally it would be great if the probabilities are according to a set distribution say

问题 As in the Dirichlet clustering, the dirichlet process can be represented by the following: Chinese Restaurant Process Stick Breaking Process Poly Urn Model For instance, if we consider Chinese Restaurant Process the process is as follows: Initially the restaurant is empty The first person to enter (Alice) sits down at a table (selects a group). The second person to enter (Bob) sits down at a table. Which table does he sit at? He sits down at a new table with probability α/(1+α) He sits with

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

问题 My team is ready to release the first version of our Linux program and would like to do so in the easiest possible manner (for users). The program needs no external files, no install, and is statically linked. ELF 32-bit LSB executable, Intel 80386, version 1 (GNU/Linux), statically linked, for GNU/Linux 2.6.15, not stripped My question is whether such a binary will run on most Linux platforms (Ubuntu, Redhat, OpenSuse, etc)? I'm also a bit concerned about the part where it says 'for GNU

问题 I would like to sample from an arbitrary function in Python. In Fast arbitrary distribution random sampling it was stated that one could use inverse transform sampling and in Pythonic way to select list elements with different probability it was mentioned that one should use inverse cumulative distribution function. As far as I undestand those methods only work the univariate case. My function is multivariate though and too complex that any of the suggestions in https://stackoverflow.com/a

问题 Goal I am trying to build a function that draws a specific number of random numbers from an an "incomplete uniform distribution". What do I call an incomplete uniform distribution? I call an incomplete uniform distribution a probability distribution where each value of X within a series of boundaries have an equal probability to be picked. In other words it is a uniform distribution with holes (where the probability is zero) as represented below x = list(12:25, 34:54, 67:90, 93:115) y = 1/sum

问题 Is there a density function for the two-piece Normal distribution: on CRAN? Thought I would check before I code one. I have checked the distribution task view. It is not listed there. I have looked in a couple of likely packages, but to no avail. Update: I have added dsplitnorm , psplitnorm , qsplitnorm and rsplitnorm functions to the fanplot package. 回答1: If you choose to construct your own version of the distribution, you might be interested in distr . It (and the related packages distrEx ,

问题 I am using Python as a plug-in scripting language for an existing C++ application. I am able to embed the python interpreter as stated in the Python documentation. Everything works successfully with the initialization and de-initialization of the interpreter. I am, however, having trouble loading modules because I have not been able to zip up the standard library in to a zip file (normally PythonXX.zip, corresponding to the version number of the python dll). What is the simplest way to zip up

问题 A 3rd party has developed an App for private use in our enterprise environment. Now its come time to distribute it and they don't want to send us their code. I have a feeling this will cause an issue because the distribution profile and certificate would have to be sent to them compromising our private key which we couldn't allow. Is this correct or can this be done without compromising our private key. Edit: as a side note, I'm ok with sending them a private key if it only is useable per App

问题 I have a model of the form: y = x + noise. I know the distribution of 'y' and of the noise and would like to have the distribution of 'x'. So I tried to deconvolve the distributions with R. I found 2 packages (decon and deamer) and I thought both methods should make more or less the same but I don't understand why deconvoluting with DeconPdf gives me a something like a normal distribution and deconvoluting with deamerKE gives me a uniform distribution. Here is an example code: library