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      <subfield code="a">Egozcue, Juan José</subfield>
      <subfield code="e">author</subfield>
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   <datafield ind2=" " ind1=" " tag="720">
      <subfield code="a">Pawlowsky-Glahn, Vera</subfield>
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      <subfield code="c">2011-05-12</subfield>
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      <subfield code="a">Bayes theorem (discrete case) is taken as a paradigm of information acquisition. As mentioned&#xd;
by Aitchison, Bayes formula can be identified with perturbation of a prior probability vector&#xd;
and a discrete likelihood function, both vectors being compositional. Considering prior, posterior&#xd;
and likelihood as elements of the simplex, a natural choice of distance between them is the&#xd;
Aitchison distance. Other geometrical features can also be considered using the Aitchison geometry.&#xd;
For instance, orthogonality in the simplex allows to think of orthogonal information, or the&#xd;
perturbation-difference to think of opposite information. The Aitchison norm provides a size of&#xd;
compositional vectors, and is thus a natural scalar measure of the information conveyed by the&#xd;
likelihood or captured by a prior or a posterior. It is called evidence information, or e-information&#xd;
for short.&#xd;
In order to support such e-information theory some principles of e-information are discussed.&#xd;
They essentially coincide with those of compositional data analysis. Also, a comparison of these&#xd;
principles of e-information with the axiomatic Shannon-information theory is performed. Shannoninformation&#xd;
and developments thereof do not satisfy scale invariance and also violate subcompositional&#xd;
coherence. In general, Shannon-information theory follows the philosophy of amalgamation&#xd;
when relating information given by an evidence-vector and some sub-vector, while the dimension&#xd;
reduction for the proposed e-information corresponds to orthogonal projections in the simplex. The&#xd;
result of this preliminary study is a set of properties of e-information that may constitute the basis&#xd;
of an axiomatic theory. A synthetic example is used to motivate the ideas and the subsequent&#xd;
discussion</subfield>
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      <subfield code="a">Estadística matemàtica -- Congressos</subfield>
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      <subfield code="a">Mathematical statistics -- Congresses</subfield>
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      <subfield code="a">Anàlisi multivariable -- Congressos</subfield>
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      <subfield code="a">Multivariate analysis -- Congresses</subfield>
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      <subfield code="a">Bayesian statistical decision theory -- Congresses</subfield>
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      <subfield code="a">Evidence Information in Bayesian Updating</subfield>
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