Hello,
I'm trying to set up a custom distribution with the following function
describing its PDF:
def pdf(x, beta, alpha, eta):
t0 = beta / (alpha * (1 - x - eta))
t1 = pow(np.log(1 - x - eta) / alpha, beta - 1)
t2 = np.exp(-pow(np.log(1 - x - eta) / alpha, beta))
return t0 * t1 * t2
It's a particular parameterization of the Log-Weibull distribution, with
beta and alpha being the shape and scale parameters, respectively, and eta
acts as a shift parameter representing the upper bound of the sample space,
which is then defined as -inf < x < eta. I'm interested in drawing random
variates, computing the above PDF, and CDF.
I have limited experience with sympy, and none with sympy.stats, but it
seems as if what I need is sympy.stats.ContinuousDistributionHandmade.
However, I'd appreciate any pointers.
Thanks,
--
Seb
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