probability

来源： https://stackoverflow.com/questions/63850562/infinite-scroll-algorithm-for-random-items-with-different-weight-probability-t

来源： https://stackoverflow.com/questions/50908047/sampling-from-multivariate-customised-cumulative-distribution-function-in-matlab

来源： https://stackoverflow.com/questions/50908047/sampling-from-multivariate-customised-cumulative-distribution-function-in-matlab

来源： https://stackoverflow.com/questions/45776177/how-to-calculate-total-statistical-distance-in-python

来源： https://stackoverflow.com/questions/45776177/how-to-calculate-total-statistical-distance-in-python

来源： https://stackoverflow.com/questions/31129592/how-to-get-a-classifiers-confidence-score-for-a-prediction-in-sklearn

来源： https://stackoverflow.com/questions/56777211/how-to-sample-from-a-step-function-in-python-scipy-numpy-etc

来源： https://stackoverflow.com/questions/56777211/how-to-sample-from-a-step-function-in-python-scipy-numpy-etc

问题 I have a simple question regarding the sample function in R. I'm randomly sampling from 0s and 1s and summing them together, from an input vector of length 5, which designates the number of trials to run and sets the seed to generate reproducible random numbers. Seed works as expected, but I get different matrices of random numbers depending on what I put in the prob statement. In this case I assumed prob=NULL should be the same as prob=c(0.5,0.5). Why isn't it? vn<-c(12, 44, 9, 17, 28) > do

问题 VERY IMPORTANT EDIT: All A i are unique . The Question I have a list A of n unique objects. Each object A i has a variable percentage P i . I want to create an algorithm that generates a new list B of k objects ( k < n / 2 and in most cases k is significantly less than n / 2 . E.g. n=231 , k=21 ). List B should have no duplicates and will be populated with objects originating from list A with the following restriction: The probability that an object A i appears in B is P i . What I Have Tried