probability

I'm looking for the best way of implementing random number generator, that will allow me to have control over probability from what range the generated number will be returned. To visualize what I'm trying to achieve I have a picture : So to summarize : Let's say that my range is 400. At the beginning I'd like to have 5% probability of getting number 0-20. But at some moment in time I'd like to have this probability increased up to 50%. Hope you get the idea. Hmm, working on your original I had a pretty simple algorithm to generate ranges in an array in the appropriate proportion, then

I want to simulate N-sided biased die? def roll(N,bias): '''this function rolls N dimensional die with biasing provided''' # do something return result >> N=6 >> bias=( 0.20,0.20,0.15,0.15,0.14,0.16,) >> roll(N,bias) 2 Yuval Adam A little bit of math here. A regular die will give each number 1-6 with equal probability, namely 1/6 . This is referred to as uniform distribution (the discrete version of it, as opposed to the continuous version). Meaning that if X is a random variable describing the result of a single role then X~U[1,6] - meaning X is distributed equally against all possible

I red a lot in the forum about this, but all answers were so specific to the the asked question. The nearest one I found to my need was: Probability Random Number Generator by Alon Gubkin . The difference is that, Alon ask to give a one face (which is six) extra chance. In my case, I want to divide the chance for the six faces so that they add up to 100%. For example, face 1 has chance of 40%, face 2 has only 10%, face 3 has 25%, ... etc. How can I do that? The single probability check with linear probability can be easily done with: function checkWithProbability($probability=0.1, $length

I have a problem where I want to generate a set of random integer values between 1 and 5 inclusive using a probability distribution. Poisson and Inverse Gamma are two distributions that show the characteristics I am after (majority at mean, less higher numbers) that I have found. I am looking at using Apache Commons Math but I wasn't sure how to generate the numbers I wanted using the distributions available. From your problem description, it sounds like you actually want a sample generated from a discrete probability distribution, and you can use EnumeratedIntegerDistribution for this purpose

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 are What is the strategy that maximizes your probability of finding the exact median? What is that

I have several data sets (distribution) as follows: set1 = [1,2,3,4,5] set2 = [3,4,5,6,7] set3 = [1,3,4,5,8] How do I plot a scatter plot with the data sets above with the y-axis being the probability (i.e. the percentile of the distribution in set: 0%-100% ) and the x-axis being the data set names? in JMP, it is called 'Quantile Plot'. Something like image attached: Please educate. Thanks. [EDIT] My data is in csv as such: Using JMP analysis tool, I'm able to plot the probability distribution plot (QQ-plot/Normal Quantile Plot as figure far below): I believe Joe Kington almost has my problem