Bayesian ML explained: distribution estimates beat point estimates for uncertain decisions
mdancho84 · x · 2026-09-19
Part 5 of mdancho84's Bayesian machine learning series: whenever true confidence and probabilistic decision-making are needed, Bayesian methods are the answer. Two key applications: uncertainty modeling (estimating full distributions instead of point estimates) and time-series analysis, where future uncertainty is crucial. The author also criticized frequentist approaches for pre-assuming fixed distributions like the Gaussian.
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