I Have a variable which is Pareto-ly distributed 'x', with unknown alpha and m.I want to find out the distribution of its mean, so I use the following model:with pymc3.Model() as model:        alpha = pymc3.Uniform('alpha', 0, 0.2)        m = pymc3.Uniform('m', 0, 2)        x = pymc3.Pareto('x', alpha = alpha, m = m, observed = obs)        mean_x = pymc3.Deterministic('mean_x', tt.mean(x))        step = pymc3.Metropolis()        trace = pymc3.sample(20000, step=step)        burned_trace=trace[1000:]But I only get a single value for 'mean_x', 'alpha' and 'm' every time I sample, and both 'alpha' and 'm' converge to the mean of their respective uniform distribution('alpha' converges to 0.1 and 'm' to 1. What is the correct way to sample the mean of a Pareto-ly distributed random variable?Thanks
How to use pymc3 to sample the mean of a Pareto random variable?
python;bayesian;monte carlo