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The key idea behind the probabilistic framework to machine learning is that learning can be thought of as inferring plausible models to explain observed data. A machine can use such models to make ...

The Annals of Applied Statistics, Vol. 14, No. 1 (March 2020), pp. 241-256 (16 pages) ABOUZAHR, C., CLELAND, J., COULLARE, F., MACFARLANE, S. B., NOTZON, F. C., SETEL ...

Bayesian networks, also known as Bayes nets, belief networks, or decision networks, are a powerful tool for understanding and reasoning about complex systems under uncertainty. They are essentially ...

When our brains don't have a good intuition for reasoning with numbers, explicit probabilistic thinking can lead to improved decision-making. A man went on an airplane ride. Unfortunately, he fell out ...