aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
If lambda = 1, then the objective function approximates the portfolio's
geometric mean return, and that's something one should maximize
with respect to the weights (which by construction should sum to 1).
Fix approved by owner:
modified: Ch6_Priors_PyMC2.ipynb
modified: Ch6_Priors_PyMC3.ipynb
and these notebooks need to be in trusted state later.
Substitute "max" for "min".
If lambda = 1, then the objective function approximates the portfolio's geometric mean return, and that's something one should maximize with respect to the weights (which by construction should sum to 1).
Fix approved by owner: modified: Ch6_Priors_PyMC2.ipynb modified: Ch6_Priors_PyMC3.ipynb and these notebooks need to be in trusted state later.