I. J. Good. Maximum Entropy for Hypothesis Formulation, Especially for Multidimensional Contingency Tables. Annals of Mathematical Statistics, Volume 34, pages 911--934, 1963.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....be regarded as the atoms of the conditional logical structure of a distribution, and products of elementary ratios provide a suitable means to investigate conditional structures. In statistics, logarithms of such expressions are used to measure the interactions between the variables involved (cf. [7], 24] Informally, the principle of conditional preservation is to state that all products of elementary ratios shall remain unchanged if there is no indication 4 for change found in the set R, representing new conditional information. As a consequence, all (statistical) interactions between ....

I.J. Good. Maximum entropy for hypothesis formulation, especially for multidimensional contingency tables. Ann. Math. Statist., 34:911--934, 1963.

....evidence, it is to create a probabilistic model which is consistent with the evidence, but otherwise, is as uniform as possible. In the task of Word Sense Disambiguation, the goal is to estimate the probability of sense s occurring with context c , p(s; c) The Principle of Maximum Entropy [42] states that the correct distribution p(s,c) is that which maximizes entropy, or uncertainty , subject to the constraints, which represent evidence [85] More precisely, if S denotes the set of possible senses, and C denotes the set of possible contexts, p should maximize the entropy H(p) ....

I. J. Good. Maximum entropy for hypothesis formulation, especially for multidimensional contingency tables. The Annals of Mathematical Statistics, pages 911--934, 1963.

.... no constraints) The Kullback Liebler distance measures the assymetric distance between two probability functions, P(x) and Q(x) It is defined as D(Q(x) P (x) j X x Q(x) log Q(x) P (x) 44) ffl We should build a model which assumes a lack of higher order interaction among the constraints [10]. In other words, if we want to specify that the constraints K cb and Kms have a special interaction, then we would have to define a new constraint K cbms . ffl We should build the model with the flattest possible probability distribution. The accepted measure of the concept of the amount of ....

Good, I. J. Maximum entropy for hypothesis formulation, especially for multidimensional contingency tables. Annals of Mathematical Statistics 34 (1963), 911--934.

....several important advantages: 1. The ME principle is simple and intuitively appealing. It imposes all of the constituent constraints, but assumes nothing else. For the special case of constraints derived from marginal probabilities, it is equivalent to assuming a lack of higher order interactions [Good 63] 2. ME is extremely general. Any probability estimate of any subset of the event space can be used, including estimates that were not derived from the data or that are inconsistent with it. Many other knowledge sources can be incorporated, such as distance dependent correlations and complicated ....

I. J. Good. Maximum Entropy for Hypothesis Formulation, Especially for Multidimensional Contingency Tables. Annals of Mathematical Statistics, Volume 34, pages 911--934, 1963.

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I. J. Good. Maximum Entropy for Hypothesis Formulation, Especially for Multidimensional Contingency Tables. Annals of Mathematical Statistics, Volume 34, pages 911--934, 1963.

No context found.

Good, I. J. Maximum entropy for hypothesis formulation, especially for multidimensional contingency tables. Annals of Mathematical Statistics 34 (1963), 911-934.

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