random-sample

问题 I am trying to generate 100 uniform random numbers in range [0.005, 0.008] with sum of one. I was looking to several questions which were relevant to my concerns but I did not find my answer. Could anyone give me a suggestion? 回答1: To start, I'm going to slightly modify your example, assuming the 100 variables are bounded by [0.008, 0.012] and that they sum to 1 (this ensures there are feasible points in the set you're sampling). The "hit and run" algorithm uniformly samples over a bounded

来源： https://stackoverflow.com/questions/32576914/random-sample-of-paired-lists-in-python

来源： https://stackoverflow.com/questions/32576914/random-sample-of-paired-lists-in-python

来源： https://stackoverflow.com/questions/35117014/generating-a-random-uniformly-distributed-real-number-in-c

来源： https://stackoverflow.com/questions/5602488/random-picks-from-permutation-generator

问题 I'm sampling a Poisson process at a millisecond time scale where the rate is not fixed. I discretise the sampling process by checking in each interval of size delta whether there is an event there or not based on the average rate in that interval. Since I'm using Python it's running a bit slower than I would hope it to be. The code I'm currently using is the following: import numpy def generate_times(rate_function,max_t,delta): times = [] for t in numpy.arange(delta,max_t,delta): avg_rate =

问题 I am an extreme beginner in Python and I am having a difficulty writing a very simple code. I am trying to write a simple code to generate 100 normally distributed number by using the function gauss with expectation 1.0 and standard deviation 0.005, and later store in an array that can be used to calculate the mean and standard deviation from those 100 sample. Here is my code: def uniformrandom(n): i=0 while i< n: gauss(1.0, 0.005) i = i + 1 return i Then I tried L = uniformrandom(100) The

问题 I am an extreme beginner in Python and I am having a difficulty writing a very simple code. I am trying to write a simple code to generate 100 normally distributed number by using the function gauss with expectation 1.0 and standard deviation 0.005, and later store in an array that can be used to calculate the mean and standard deviation from those 100 sample. Here is my code: def uniformrandom(n): i=0 while i< n: gauss(1.0, 0.005) i = i + 1 return i Then I tried L = uniformrandom(100) The

问题 I am an extreme beginner in Python and I am having a difficulty writing a very simple code. I am trying to write a simple code to generate 100 normally distributed number by using the function gauss with expectation 1.0 and standard deviation 0.005, and later store in an array that can be used to calculate the mean and standard deviation from those 100 sample. Here is my code: def uniformrandom(n): i=0 while i< n: gauss(1.0, 0.005) i = i + 1 return i Then I tried L = uniformrandom(100) The

问题 I would like to get 20 randomly generated numbers from a lognormal distribution with the geometric mean of 10 and geometric standard deviation of 2.5. Which R function should I use to accomplish this task? Thank you for your help! 回答1: The rlnorm function: rlnorm(20, log(10), log(2.5)) More generally distributions in R are generally available in d,p,q,r forms with those letters coming first followed by the distribution stem: norm , lnorm , unif , gamma , ... etc. Their help pages will contain