Calculating Probability of a Random Variable in a Distribution in Python
All these are very similar: If you can compute #1 using a function cdf(x)
, then the solution to #2 is simply 1 - cdf(x)
, and for #3 it's cdf(x) - cdf(y)
.
Since Python includes the (gauss) error function built in since version 2.7 you can do this by calculating the cdf of the normal distribution using the equation from the article you linked to:
import math
print 0.5 * (1 + math.erf((x - mean)/math.sqrt(2 * standard_dev**2)))
where mean
is the mean and standard_dev
is the standard deviation.
Some notes since what you asked seemed relatively straightforward given the information in the article:
- CDF of a random variable (say X) is the probability that X lies between -infinity and some limit, say x (lower case). CDF is the integral of the pdf for continuous distributions. The cdf is exactly what you described for #1, you want some normally distributed RV to be between -infinity and x (<= x).
- < and <= as well as > and >= are same for continuous random variables as the probability that the rv is any single point is 0. So whether or not x itself is included doesn't actually matter when calculating the probabilities for continuous distributions.
- Sum of probabilities is 1, if its not < x then it's >= x so if you have the
cdf(x)
. then1 - cdf(x)
is the probability that the random variable X >= x. Since >= is equivalent for continuous random variables to >, this is also the probability X > x.
Cerin
Updated on June 17, 2022Comments
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Cerin about 2 years
Given a mean and standard-deviation defining a normal distribution, how would you calculate the following probabilities in pure-Python (i.e. no Numpy/Scipy or other packages not in the standard library)?
- The probability of a random variable r where r < x or r <= x.
- The probability of a random variable r where r > x or r >= x.
- The probability of a random variable r where x > r > y.
I've found some libraries, like Pgnumerics, that provide functions for calculating these, but the underlying math is unclear to me.
Edit: To show this isn't homework, posted below is my working code for Python<=2.6, albeit I'm not sure if it handles the boundary conditions correctly.
from math import * import unittest def erfcc(x): """ Complementary error function. """ z = abs(x) t = 1. / (1. + 0.5*z) r = t * exp(-z*z-1.26551223+t*(1.00002368+t*(.37409196+ t*(.09678418+t*(-.18628806+t*(.27886807+ t*(-1.13520398+t*(1.48851587+t*(-.82215223+ t*.17087277))))))))) if (x >= 0.): return r else: return 2. - r def normcdf(x, mu, sigma): t = x-mu; y = 0.5*erfcc(-t/(sigma*sqrt(2.0))); if y>1.0: y = 1.0; return y def normpdf(x, mu, sigma): u = (x-mu)/abs(sigma) y = (1/(sqrt(2*pi)*abs(sigma)))*exp(-u*u/2) return y def normdist(x, mu, sigma, f): if f: y = normcdf(x,mu,sigma) else: y = normpdf(x,mu,sigma) return y def normrange(x1, x2, mu, sigma, f=True): """ Calculates probability of random variable falling between two points. """ p1 = normdist(x1, mu, sigma, f) p2 = normdist(x2, mu, sigma, f) return abs(p1-p2)
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Cerin over 12 yearsHow are the bounds interpreted? You say cdf(x) solves #1, but I have two separate cases for #1. Less than and less than or equal to. Which does cdf(x) solve, and how would I find the other case?
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ameer over 12 yearsHi, for normal distribution which is continuous, less than and less than equal to are equivalent so this is just one case. I've added some notes.
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jfs over 12 years
1 - cdf(x)
could be expressed viamath.erfc()
. It might improve precision forcdf(x) near 1
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hephestos about 11 yearsthe notes placed under, beat my university notes. small, clean, elegant.