Predictive updating methods with application to bayesian classification

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As Bayesians, we start with a belief, called a prior.Then we obtain some data and use it to update our belief. Should we obtain even more data, the old posterior becomes a new prior and the cycle repeats.

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However, they tend to be rather similar to each other, all being variants of Stochastic Gradient Descent.By the way, these probabilities are only statements of belief from a classifier.Whether they correspond to real probabilities is another matter completely and it’s called calibration.Imagine a statistician meticulously constructing and tweaking a model using what little data he has.In this setting you spare no effort to make the best use of available input.

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