sampling

问题 So from answers to the question, Randomly Generating Combinations From Variable Weights, I've managed to sample without replacement given unequal probabilities. I've implemented this in my particular context and it works great. Now I have access to additional information about my distribution. Specifically, I've been using a neural network to generate these probabilities so far, and I've now trained my neural network to output a probability for each pair of unique objects, in addition to

问题 So from answers to the question, Randomly Generating Combinations From Variable Weights, I've managed to sample without replacement given unequal probabilities. I've implemented this in my particular context and it works great. Now I have access to additional information about my distribution. Specifically, I've been using a neural network to generate these probabilities so far, and I've now trained my neural network to output a probability for each pair of unique objects, in addition to

问题 I am trying to sample (convert analog to digital) mp3 files via the following Python code using the librosa library, but it takes too much time (around 4 seconds for one file). I suspect this is because librosa doesn't support mp3 and hence uses the slower audioread to sample mp3 Code: import time import librosa s = time.time() for i in mp3_list[:10]: # list of mp3 file paths, doing for 10 files y, sr = librosa.load(i) print('time taken =', time.time() - s) time taken = 36.55561399459839 I

问题 The Problem: I am attempting to use R to generate a random study design where half of the participants are randomly assigned to "Treatement 1" and the other half are assigned to "Treatment 2". However, because half of the subjects are male and half are female and I also want to ensure that an equal number of males and females are exposed to each treatment, half of the males and females should be assigned to "Treatment 1" and the remaining half should be assigned to "Treatment 2". There are

问题 I have a list of 10 probabilities (assume these are sorted in descending order): <p1, p2, ..., p10> . I want to sample (without replacement) 10 elements such that the probability of selecting i -th index is p_i. Is there a ready to use Java method in common libraries like Random, etc that I could use to do that? Example: 5-element list: <0.4,0.3,0.2,0.1,0.0> Select 5 indexes (no duplicates) such that their probability of selection is given by the probability at that index in the list above.

问题 I have a list of 10 probabilities (assume these are sorted in descending order): <p1, p2, ..., p10> . I want to sample (without replacement) 10 elements such that the probability of selecting i -th index is p_i. Is there a ready to use Java method in common libraries like Random, etc that I could use to do that? Example: 5-element list: <0.4,0.3,0.2,0.1,0.0> Select 5 indexes (no duplicates) such that their probability of selection is given by the probability at that index in the list above.

问题 I have a list of 10 probabilities (assume these are sorted in descending order): <p1, p2, ..., p10> . I want to sample (without replacement) 10 elements such that the probability of selecting i -th index is p_i. Is there a ready to use Java method in common libraries like Random, etc that I could use to do that? Example: 5-element list: <0.4,0.3,0.2,0.1,0.0> Select 5 indexes (no duplicates) such that their probability of selection is given by the probability at that index in the list above.

问题 I always thought set.seed() only makes random variable generators (e.g., rnorm ) to generate a unique sequence for any specific set of input values. However, I'm wondering, why when we set the set.seed() , then the function sample() doesn't do its job correctly? Question Specifically, given the below example, is there a way I can use set.seed before the rnorm but sample would still produce new random samples from this rnorm if sample is run multiple times? Here is an R code: set.seed(123458)

问题 I always thought set.seed() only makes random variable generators (e.g., rnorm ) to generate a unique sequence for any specific set of input values. However, I'm wondering, why when we set the set.seed() , then the function sample() doesn't do its job correctly? Question Specifically, given the below example, is there a way I can use set.seed before the rnorm but sample would still produce new random samples from this rnorm if sample is run multiple times? Here is an R code: set.seed(123458)

问题 I was wondering how I could check via simulation in R that the 95% Confidence Interval obtained from a binomial test with 5 successes in 15 trials when TRUE p = .5 has a 95% "Coverage Probability" in the long-run? Here is the 95% CI for such a test using R (how can show that the following CI has a 95% coverage in the long-run if TRUE p = .5 ): as.numeric(binom.test(x = 5, n = 15, p = .5)[[4]]) # > [1] 0.1182411 0.6161963 (in the long-run 95% of the time, ".5" is contained within these # two