Evidence functions: a compositional approach to information

The discrete case of Bayes’ formula is considered the paradigm of information acquisition. Prior and posterior probability functions, as well as likelihood functions, called evidence functions, are compositions following the Aitchison geometry of the simplex, and have thus vector character. Bayes’ formula becomes a vector addition. The Aitchison norm of an evidence function is introduced as a scalar measurement of information. A fictitious fire scenario serves as illustration. Two different inspections of affected houses are considered. Two questions are addressed: (a) which is the information provided by the outcomes of inspections, and (b) which is the most informative inspection.Peer ReviewedPostprint (author's final draft

Long-term impact of fecal transplantation in healthy volunteers

Fecal microbiota transplantation (FMT) has been recently approved by FDA for the treatment of refractory recurrent clostridial colitis (rCDI). Success of FTM in treatment of rCDI led to a number of studies investigating the effectiveness of its application in the other gastrointestinal diseases. However, in the majority of studies the effects of FMT were evaluated on the patients with initially altered microbiota. The aim of our study was to estimate effects of FMT on the gut microbiota composition in healthy volunteers and to monitor its long-term outcomes.Peer ReviewedPostprint (published version

Space-time compositional models: an introduction to simplicial partial differential operators

A function assigning a composition to space-time points is called a compositional or simplicial field. These fields can be analysed using the compositional analysis tools. A study of linear models for evolutionary compositions depending on one variable, usually time, was formulated by Egozcue and Jarauta-Bragulat (2014) in terms of the so-called simplicial linear differential equations. The foundations of differential and integral calculus for simplex-valued functions of one real variable, was presented by Egozcue, Jarauta-Bragulat and Díaz-Barrero (2011). In order to study compositions depending on space and/or time, reformulation and interpretation of traditional partial differential operators is required. These operators such as: partial derivatives, compositional gradient, directional derivative and divergence are of primary importance to state alternative models of processes as diffusion, advection and waves, from the compositional perspective. This kind of models, usually based on continuity of mass, circulation of a vector field along a curve and flux through surfaces, should be analyzed when compositional operators are used instead of the traditional gradient or divergence. This study is aimed at setting up the definitions, mathematical basis and interpretation of such operators.Peer ReviewedPostprint (author's final draft

Entropic rectification and current inversion in a pulsating channel

We show the existence of a resonant behavior of the current of Brownian particles confined in a pulsating channel. The interplay between the periodic oscillations of the shape of the channel and a force applied along its axis leads to an increase of the particle current as a function of the diffusion coefficient. A regime of current inversion is also observed for particular values of the oscillation frequency and the applied force. The model proposed is based on the Fick-Jacobs equation in which the entropic barrier and the effective diffusion coefficient depend on time. The phenomenon observed could be used to optimize transport in microfluidic devices or biological channels

Evidence information in Bayesian updating

Bayes theorem (discrete case) is taken as a paradigm of information acquisition. As men-tioned by Aitchison, Bayes formula can be identified with perturbation of a prior probability vector and a discrete likelihood function, both vectors being compositional. Considering prior, poste-rior and likelihood as elements of the simplex, a natural choice of distance between them is the Aitchison distance. Other geometrical features can also be considered using the Aitchison geom-etry. For instance, orthogonality in the simplex allows to think of orthogonal information, or the perturbation-difference to think of opposite information. The Aitchison norm provides a size of compositional vectors, and is thus a natural scalar measure of the information conveyed by the likelihood or captured by a prior or a posterior. It is called evidence information, or e-information for short. In order to support such e-information theory some principles of e-information are discussed. They essentially coincide with those of compositional data analysis. Also, a comparison of these principles of e-information with the axiomatic Shannon-information theory is performed. Shannon-information and developments thereof do not satisfy scale invariance and also violate subcomposi-tional coherence. In general, Shannon-information theory follows the philosophy of amalgamation when relating information given by an evidence-vector and some sub-vector, while the dimension reduction for the proposed e-information corresponds to orthogonal projections in the simplex. The result of this preliminary study is a set of properties of e-information that may constitute the basis of an axiomatic theory. A synthetic example is used to motivate the ideas and the subsequent discussion