probability

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.length); return notRandomNumbers[idx]; } states in the comment "create notRandomNumbers dynamically (given

I want to generate a random number with a given probability but I'm not sure how to: I need a number between 1 and 3 num = ceil(rand*3); but I need different values to have different probabilities of generating eg. 0.5 chance of 1 0.1 chance of 2 0.4 chance of 3 I'm sure this is straightforward but I can't think of how to do it. The simple solution is to generate a number with a uniform distribution (using rand ), and manipulate it a bit: r = rand; prob = [0.5, 0.1, 0.4]; x = sum(r >= cumsum([0, prob])); or in a one-liner: x = sum(rand >= cumsum([0, 0.5, 0.1, 0.4])); Explanation Here r is a

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 continuous distribution. If this is not possible, are there any python modules that take in probability

I am new to R hence asking here (haven't been able to find very helpful tutorials for simulation that are detailed.) The problem statement is this Simulate a coin toss for 20 times and record the number of heads & longest run of heads. Simulate a coin toss and record the number of flips necessary until 2,3,4 heads occur in sequence (consecutively) (negative binomial?) Make 100 runs with different seeds to find the distribution of items recorded. How does one go about solving this in the programming language R ? For 1, I did the following: n=20 #no of trials y=NULL #initializing a vector of

This is a very hard but interesting probability question related to Knuth shuffle. When looping for each element, the swap is performed for the current element with any random element from the whole array (not within the elements left), then what is the probabilty of the original i th element ending up at the j th position? The Knuth shuffle is as follows (in Python, but it could be pseudocode) for i in range(len(v)): swap(v, i, randrange(i, len(v)) The naïve shuffle is very similar, but it is not the Knuth shuffle: for i in range(len(v)): swap(v, i, randrange(0, len(v)) The Knuth shuffle

I am using the following code operation=[rand(1,noOfNodes)>prob]; to generate 1 and zeros ( noOfNodes times). If I use prob=0.2 and try 100 values there exist in some cases 40 zeros. Isn't it weird? I need the probability of getting zeros less than 0.2 No, that's not weird. That's probability for ya. If you flip a coin 100 times, you don't always get 50 heads and 50 tails. Sometimes you get 49 and 51, and on that rarest of occasions you can even get the same one 100 times. With your above code you're not guaranteed to always get 20 zeroes and 80 ones when noOfNodes is 100. If you want to

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 and invasive grey squirrel). I wanted to see what affected detection (or non detection) of the various

I do not understand the output of the SVM classifier from the Spark MLLib algorithm. I want to convert the score to a probability, so that I get a probability for a data-point belonging to a certain class (on which the SVM is trained, a.k.a. multi-class problem) (see also this thread) . It is unclear what the score means. Is it the distance to the hyperplane? How do I get the probabilities from it? import org.apache.spark.mllib.classification.{SVMModel, SVMWithSGD} import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics import org.apache.spark.mllib.util.MLUtils // Load training