encoding is gonna cost you at least entropy may be more it's gonna cost you an additional decay all the versions in P and P hat so the price you pay is a cal divergence we know that the log likely objective when we learn a genera t'v model I should comes down to minimizing the KL divergence between the data distribution and the model that you learn and selectively when we're maximizing log likelihood we're minimizing this calorie version effectively trying to find a distribution now incur minimal overhead if we use it for an encoding