نتایج جستجو برای: jeffreys
تعداد نتایج: 514 فیلتر نتایج به سال:
We introduce a novel parametric family of symmetric information-theoretic distances based on Jensen’s inequality for a convex functional generator. In particular, this family unifies the celebrated Jeffreys divergence with the Jensen-Shannon divergence when the Shannon entropy generator is chosen. We then design a generic algorithm to compute the unique centroid defined as the minimum average d...
The last line follows from the previous line by a second application of the same Jensen inequality. Since the J-divergence ranges between zero and positive in nity, whereas the Jensen-Shannon divergence ranges between zero and ln 2 [i.e. 1 bit], this inequality has the correct limits for identical (pi = qi, JS(p;q) = Jeffreys(p;q) = 0) and orthogonal (piqi = 0, JS(p;q) = ln 2, Jeffreys(p;q) = +...
Ronald Fisher advocated testing using p-values, Harold Jeffreys proposed use of objective posterior probabilities of hypotheses and Jerzy Neyman recommended testing with fixed error probabilities. Each was quite critical of the other approaches. Most troubling for statistics and science is that the three approaches can lead to quite different practical conclusions. This article focuses on discu...
In this paper we introduce a new sparseness inducing prior which does not involve any (hyper)parameters that need to be adjusted or estimated. Although other applications are possible, we focus here on supervised learning problems: regression and classification. Experiments with several publicly available benchmark data sets show that the proposed approach yields state-of-the-art performance. I...
There are several ways to parameterize a distribution belonging to an exponential family, each one leading to a different Bayesian analysis of the data under standard conjugate priors. To overcome this problem, we propose a new class of conjugate priors which is invariant with respect to smooth reparameterization. This class of priors contains the Jeffreys prior as a special case, according to ...
We analyze the relationship between a Minimum Description Length (MDL) estimator (posterior mode) and a Bayes estimator for exponential families. We show the following results concerning these estimators: a) Both the Bayes estimator with Jeffreys prior and the MDL estimator with the uniform prior with respect to the expectation parameter are nearly equivalent to a bias-corrected maximum-likelih...
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