binomial-cdf | 易学教程

R, use binomial distribution with more than two possibilities

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问题 I know this is probably elementary, but I seem to have a mental block. Let's say you want to calculate the probability of tossing a 4, 5, or 6 on a roll of one die. In R, it's easy enough: sum(1/6, 1/6, 1/6) This gives 1/2 which is the correct answer. However, I have in the back of my mind (where it possibly should remain) that I should be able to use the binomial distribution for this. I've tried various combinations of arguments for pbinom and dbinom, but I can't get the right answer. With

Beta Binomial Function in Python

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问题 I would like to calculate the probability given by a binomial distribution for predetermined x(successes), n(trials), and p(probability) - the later of which is given by a probability mass function Beta(a,b). I am aware of scipy.stats.binom.pmf(x,n,p) - but I am unsure how I can replace p with a probability function. I am also wondering whether I could use the loc argument of scipy.stats.binom.pmf to emulate this behaviour. 回答1: Wiki says that the compound distribution function is given by f

Beta Binomial Function in Python

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I would like to calculate the probability given by a binomial distribution for predetermined x(successes), n(trials), and p(probability) - the later of which is given by a probability mass function Beta(a,b). I am aware of scipy.stats.binom.pmf(x,n,p) - but I am unsure how I can replace p with a probability function. I am also wondering whether I could use the loc argument of scipy.stats.binom.pmf to emulate this behaviour. Wiki says that the compound distribution function is given by f(k|n,a,b) = comb(n,k) * B(k+a, n-k+b) / B(a,b) where B is the beta function, a and b are the original Beta

How can I efficiently calculate the binomial cumulative distribution function?

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Let's say that I know the probability of a "success" is P. I run the test N times, and I see S successes. The test is akin to tossing an unevenly weighted coin (perhaps heads is a success, tails is a failure). I want to know the approximate probability of seeing either S successes, or a number of successes less likely than S successes. So for example, if P is 0.3, N is 100, and I get 20 successes, I'm looking for the probability of getting 20 or fewer successes. If, on the other hadn, P is 0.3, N is 100, and I get 40 successes, I'm looking for the probability of getting 40 our more successes.

Binomial coefficient

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问题 'Simple' question, what is the fastest way to calculate the binomial coefficient? - Some threaded algorithm? I'm looking for hints :) - not implementations :) 回答1: According to the equation below (from wikipedia) the fastest way would be to split the range i=1,k to the number of threads, give each thread one range segment, and each thread updates the final result in a lock. "Academic way" would be to split the range into tasks, each task being to calculate (n - k + i)/i, and then no matter

How can I efficiently calculate the binomial cumulative distribution function?

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问题 Let's say that I know the probability of a "success" is P. I run the test N times, and I see S successes. The test is akin to tossing an unevenly weighted coin (perhaps heads is a success, tails is a failure). I want to know the approximate probability of seeing either S successes, or a number of successes less likely than S successes. So for example, if P is 0.3, N is 100, and I get 20 successes, I'm looking for the probability of getting 20 or fewer successes. If, on the other hadn, P is 0