How can I efficiently calculate the binomial cumulative distribution function?

Question

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

Answer 1

Try this one, used in GMP. Another reference is this.

Answer 2

I was on a project where we needed to be able to calculate the binomial CDF in an environment that didn't have a factorial or gamma function defined. It took me a few weeks, but I ended up coming up with the following algorithm which calculates the CDF exactly (i.e. no approximation necessary). Python is basically as good as pseudocode, right?

import numpy as np

def binomial_cdf(x,n,p):
    cdf = 0
    b = 0
    for k in range(x+1):
        if k > 0:
            b += + np.log(n-k+1) - np.log(k) 
        log_pmf_k = b + k * np.log(p) + (n-k) * np.log(1-p)
        cdf += np.exp(log_pmf_k)
    return cdf

Performance scales with x. For small values of x, this solution is about an order of magnitude faster than scipy.stats.binom.cdf, with similar performance at around x=10,000.

I won't go into a full derivation of this algorithm because stackoverflow doesn't support MathJax, but the thrust of it is first identifying the following equivalence:

• For all k > 0, sp.misc.comb(n,k) == np.prod([(n-k+1)/k for k in range(1,k+1)])

Which we can rewrite as:

• sp.misc.comb(n,k) == sp.misc.comb(n,k-1) * (n-k+1)/k

or in log space:

• np.log( sp.misc.comb(n,k) ) == np.log(sp.misc.comb(n,k-1)) + np.log(n-k+1) - np.log(k)

Because the CDF is a summation of PMFs, we can use this formulation to calculate the binomial coefficient (the log of which is b in the function above) for PMF_{x=i} from the coefficient we calculated for PMF_{x=i-1}. This means we can do everything inside a single loop using accumulators, and we don't need to calculate any factorials!

The reason most of the calculations are done in log space is to improve the numerical stability of the polynomial terms, i.e. p^x and (1-p)^(1-x) have the potential to be extremely large or extremely small, which can cause computational errors.

EDIT: Is this a novel algorithm? I've been poking around on and off since before I posted this, and I'm increasingly wondering if I should write this up more formally and submit it to a journal.

Answer 3

import numpy as np
np.random.seed(1)
x=np.random.binomial(20,0.6,10000) #20 flips of coin,probability of 
                                 heads percentage and 10000 times 
                                  done.
sum(x>12)/len(x)

The output is 41% of times we got 12 heads.

Answer 4

From the portion of your question "getting at least S heads" you want the cummulative binomial distribution function. See http://en.wikipedia.org/wiki/Binomial_distribution for the equation, which is described as being in terms of the "regularized incomplete beta function" (as already answered). If you just want to calculate the answer without having to implement the entire solution yourself, the GNU Scientific Library provides the function: gsl_cdf_binomial_P and gsl_cdf_binomial_Q.

Answer 5

The DCDFLIB Project has C# functions (wrappers around C code) to evaluate many CDF functions, including the binomial distribution. You can find the original C and FORTRAN code here. This code is well tested and accurate.

If you want to write your own code to avoid being dependent on an external library, you could use the normal approximation to the binomial mentioned in other answers. Here are some notes on how good the approximation is under various circumstances. If you go that route and need code to compute the normal CDF, here's Python code for doing that. It's only about a dozen lines of code and could easily be ported to any other language. But if you want high accuracy and efficient code, you're better off using third party code like DCDFLIB. Several man-years went into producing that library.

Answer 6

I think you want to evaluate the incomplete beta function.

There's a nice implementation using a continued fraction representation in "Numerical Recipes In C", chapter 6: 'Special Functions'.