Furthermore, Bayesian models are not to be applied when there is not statistical independence of observations. "LightGBM Predict on Pandas DataFrame – Column O

Furthermore, Bayesian models are not to be applied when there is not statistical independence of observations. "LightGBM Predict on Pandas DataFrame – Column Order Matters" (2025) https://news.ycombinator.com/item?id=43088854 https://news.ycombinator.com/item?id=43088854 : > [ LightGBM,] does not converge regardless of feature order. > From https://news.ycombinator.com/item?id=41873650 https://news.ycombinator.com/item?id=41873650 : >> Do algorithmic outputs diverge or converge given variance in sequence order of all orthogonal axes? Does it matter which order the dimensions are stated in; is the output sensitive to feature order, but does it converge regardless? > Also, current LLMs suggest that statistical independence is entirely distinct from orthogonality, which we typically assume with high-dimensional problems. And, many statistical models do not work with non-independent features. > Does this model work with non-independence or nonlinearity? > Does the order of the columns in the training data CSV change the alpha of the model; does model output converge regardless of variance in the order of training data? From https://news.ycombinator.com/item?id=37462132 https://news.ycombinator.com/item?id=37462132 : > [ quantum discord ] > TIL the separable states problem is considered NP-hard, and many models specify independence of observation as necessary. How does (NP-hard) quantum separability relate to statistical independence as necessary for statistical models to be appropriate? If it is so hard to determine which particles are and aren't entangled, when should we assume statistical independence of observation? If we cannot assume statistical independence of observation, we know that Bayesian models aren't appropriate.