We are living in new Bayesian age. Applications of Bayesian probability are taking over our lives. Doctors, lawyers, engineers and financiers use computerized Bayesian networks to aid their decision-making. Psychologists and neuroscientists explore the Bayesian workings of our brains. Statisticians increasingly rely on Bayesian logic. Even our email spam filters work on Bayesian principles.
While that's true, strictly speaking, it's akin to writing that, "We are living in a new logical age" when Boolean algebra was first finding wide application in engineering and the natural sciences. "Bayesian methods" just mean using statistical modeling techniques that conform to Bayes' rule as their normative guide. In "machine learning" or "frequentist" terms, it just means that good approximate-Bayesian reasoning minimizes the KL divergence between the true posterior and the approximate model (whether variational or by sampling or by training a neural network, whatever), as opposed to minimizing the classification hinge-loss or the mean squared error (though some of those losses have formally equivalent Bayesian priors).
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While that's true, strictly speaking, it's akin to writing that, "We are living in a new logical age" when Boolean algebra was first finding wide application in engineering and the natural sciences. "Bayesian methods" just mean using statistical modeling techniques that conform to Bayes' rule as their normative guide. In "machine learning" or "frequentist" terms, it just means that good approximate-Bayesian reasoning minimizes the KL divergence between the true posterior and the approximate model (whether variational or by sampling or by training a neural network, whatever), as opposed to minimizing the classification hinge-loss or the mean squared error (though some of those losses have formally equivalent Bayesian priors).
Strictly speaking, health care and genocide are the same thing, you just minimize different metrics.