Sorry for the noise, I sent this to the dev list, while it belongs to the user list. Hi list, I am looking at estimating entropy and conditional entropy from data for which I have only access to observations, and not the underlying probabilistic laws. With low dimensional data, I would simply use an empirical estimate of the probabilities by converting each observation to its quantile, and then apply the standard formula for entropy (for instance using scipy.stats.entropy). However, I have high-dimensional data (~100 features, and 30000 observations). Not only is it harder to convert observations to probabilities in the empirical law, but I am also worried of curse of dimensionality effects: density estimation in high-dimension is a difficult problem. Does anybody has advices, or code in Python to point to, for this task? Cheers, Gaël