Criticism
While MAP estimation is a limit of Bayes estimators (under the 0-1 loss function), it is not very representative of Bayesian methods in general. This is because MAP estimates are point estimates, whereas Bayesian methods are characterized by the use of distributions to summarize data and draw inferences: thus, Bayesian methods tend to report the posterior mean or median instead, together with credible intervals. This is both because these estimators are optimal under squared-error and linear-error loss respectively - which are more representative of typical loss functions - and because the posterior distribution may not have a simple analytic form: in this case, the distribution can be simulated using Markov chain Monte Carlo techniques, while optimization to find its mode(s) may be difficult or impossible.
In many types of models, such as mixture models, the posterior may be multi-modal. In such a case, the usual recommendation is that one should choose the highest mode: this is not always feasible (global optimization is a difficult problem), nor in some cases even possible (such as when identifiability issues arise). Furthermore, the highest mode may be uncharacteristic of the majority of the posterior.
Finally, unlike ML estimators, the MAP estimate is not invariant under reparameterization. Switching from one parameterization to another involves introducing a Jacobian that impacts on the location of the maximum.
As an example of the difference between Bayes estimators mentioned above (mean and median estimators) and using an MAP estimate, consider the case where there is a need to classify inputs as either positive or negative (for example, loans as risky or safe). Suppose there are just three possible hypotheses about the correct method of classification, and with posteriors 0.4, 0.3 and 0.3 respectively. Suppose given a new instance, classifies it as positive, whereas the other two classify it as negative. Using the MAP estimate for the correct classifier, is classified as positive, whereas the Bayes estimators would average over all hypotheses and classify as negative.
Read more about this topic: Maximum A Posteriori Estimation
Famous quotes containing the word criticism:
“A bad short story or novel or poem leaves one comparatively calm because it does not exist, unless it gets a fake prestige through being mistaken for good work. It is essentially negative, it is something that has not come through. But over bad criticism one has a sense of real calamity.”
—Rebecca West (1892–1983)
“Cubism had been an analysis of the object and an attempt to put it before us in its totality; both as analysis and as synthesis, it was a criticism of appearance. Surrealism transmuted the object, and suddenly a canvas became an apparition: a new figuration, a real transfiguration.”
—Octavio Paz (b. 1914)
“People try so hard to believe in leaders now, pitifully hard. But we no sooner get a popular reformer or politician or soldier or writer or philosopher—a Roosevelt, a Tolstoy, a Wood, a Shaw, a Nietzsche, than the cross-currents of criticism wash him away. My Lord, no man can stand prominence these days. It’s the surest path to obscurity. People get sick of hearing the same name over and over.”
—F. Scott Fitzgerald (1896–1940)