Baysian updating


26-Dec-2017 17:36

Bayesian inference computes the posterior probability according to Bayes' theorem: – the posterior probability of a hypothesis is proportional to its prior probability (its inherent likeliness) and the newly acquired likelihood (its compatibility with the new observed evidence). If the evidence does not match up with a hypothesis, one should reject the hypothesis.

But if a hypothesis is extremely unlikely a priori, one should also reject it, even if the evidence does appear to match up.

baysian updating-52

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An explicit link between Gaussian fields and Gaussian Markovrandom fields: The SPDE approach (with discussion).

In this way, you are comparing the results obtained from the same model.



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