Web Reference: Dec 6, 2020 · Because probabilistic models effectively "know what they don't know", they can help prevent terrible decisions based on unfounded extrapolations from insufficient data. As the questions we ask and the models we build become increasingly complex, the risks of insufficient data rise. Feb 7, 2017 · On the other hand, from statistical points (probabilistic approach) of view, we may emphasize more on generative models. For example, mixture of Gaussian Model, Bayesian Network, etc. The book by Murphy "machine learning a probabilistic perspective" may give you a better idea on this branch. Nov 2, 2016 · Is probabilistic inference only applicable in a graphical modelling context? What's the distinction between traditional statistical inference (p-values, confidence intervals, Bayes factors etc.) and probabilistic inference?
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