[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
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