probability

问题 I am having trouble generating random letters based on probability. For example, the letters J, K, Q, Y, Z each have a probability of 1/96 of occurring. A similar process (with higher probabilities) is used for other letters. Can somebody show me how to do this? Edit to be specific: I'm writing a method called "getRandomLetter" that returns a char of a random letter based on a probability fraction. 回答1: The typical way to select from a discrete set of elements with specific probabilities is

问题 In javascript (or jquery) is there a simple function to have four integers with their probability values: 1|0.41, 2|0.29, 3|0.25, 4|0.05 how can I generate these four numbers taking into account their probabilities ? This question is very similar to the one posted here: generate random integers with probabilities HOWEVER the solution posted there: function randomWithProbability() { var notRandomNumbers = [1, 1, 1, 1, 2, 2, 2, 3, 3, 4]; var idx = Math.floor(Math.random() * notRandomNumbers

问题 I have a list of US names and their respective names from the US census website. I would like to generate a random name from this list using the given probability. The data is here: US Census data I have seen algorithms like the roulette wheel selection algorithm that are easy to implement, but I wanted to know if there was any way to generate random names in O(1). For histogram data this is easier, as you could create a hash of integers to birthdays, but I would like to do this for a

问题 I have a list of US names and their respective names from the US census website. I would like to generate a random name from this list using the given probability. The data is here: US Census data I have seen algorithms like the roulette wheel selection algorithm that are easy to implement, but I wanted to know if there was any way to generate random names in O(1). For histogram data this is easier, as you could create a hash of integers to birthdays, but I would like to do this for a

问题 I'm extremely stuck at the moment as I am trying to figure out how to calculate the probability from my glm output in R. I know the data is very insignificant but I would really love to be shown how to get the probability from an output like this. I was thinking of trying inv.logit() but didn't know what variables to put within the brackets. The data is from occupancy study. I'm assessing the success of a hair trap method versus a camera trap in detecting 3 species (red squirrel, pine marten

问题 I need a space efficient probabilistic data structure to store values that I have already computed. For me computation is cheap but space is not - so if this data structure returns a false negative, I am okay with redoing some work every once in a while but false positives are unacceptable. So what I am looking for is sort of the opposite of a Bloom filter. 回答1: For false negative you can use lossy hash table or a LRUCache. It is a data structure with fast O(1) look-up that will only give

问题 I 'm having a problem with creating a joint density function from data. What I have is queue sizes from a stock as two vectors saved as: X = [askQueueSize bidQueueSize]; I then use the hist3-function to create a 3D histogram. This is what I get: http://dl.dropbox.com/u/709705/hist-plot.png What I want is to have the Z-axis normalized so that it goes from [0 1]. How do I do that? Or do someone have a great joint density matlab function on stock? This is similar (How to draw probability density

问题 I used a Dimensionality Reduction method (discussion here: Random projection algorithm pseudo code) on a large dataset. After reducing the dimension from 1000 to 50, I get my new dataset where each sample looks like: [ 1751. -360. -2069. ..., 2694. -3295. -1764.] Now I am a bit confused, because I don't know what negative feature values supposed to mean. Is it okay to have negative features like this? Because before the reduction, each sample was like this: 3, 18, 18, 18, 126 ... Is it normal

问题 I want to specify the probably density function of a distribution and then pick up N random numbers from that distribution in python. How do I go about doing that? 回答1: In general, you want to have the inverse cumulative probability density function. Once you have that, then generating the random numbers along the distribution is simple: import random def sample(n): return [ icdf(random.random()) for _ in range(n) ] Or, if you use NumPy: import numpy as np def sample(n): return icdf(np.random

问题 This is a spin off of this StackOverflow question. Assume that you have a fixed number k of storage locations, and space for two counters. You will receive n items in random order (all permutations of the n items are equally likely). After receiving each item you can either store it in one of the k locations (discarding one of the previously stored values), or discard the item. You can also increment or decrement either of the counters. Any discarded item cannot be retrieved. The questions