# [SciPy-user] computing Bayesian credible intervals

Neil Martinsen-Burrell nmb@wartburg....
Fri May 2 15:04:09 CDT 2008

```Johann Cohen-Tanugi <cohen <at> slac.stanford.edu> writes:

> hi Neil, thanks for your answer and sorry I was not clear enough. Of
> course I require the 2 conditions. 1) defines *a* credible interval if p
> is a posterior pdf; and 2) sets a constraint that for common situation
> yield *the* standard Bayesian credible interval. I will have a look at
> brentq, I do not know what it refers to.

scipy.optimize.brentq is Brent's method for finding a root of a given scalar
equation.  Since you are looking for two values, a and b, with two conditions,
then Brent's method is not appropriate (barring some symmetry-based reduction to
one variable).  I like to use scipy.optimize.fsolve to find roots of
multivariable equations, such as

def solve_me(x): # x is an array of the values you are solving for
a,b = x
integral_error = quad(density, a , b) - q
prob_difference = density(b) - density(a)
return np.array([integral_error, prob_difference])

fsolve(solve_me, [0.0, 1.0])  # initial guess is a = 0, b = 1

```