Recent theoretical progress on an information geometrodynamical approach to chaos

In this paper, we report our latest research on a novel theoretical information-geometric framework suitable to characterize chaotic dynamical behavior of arbitrary complex systems on curved statistical manifolds. Specifically, an information-geometric analogue of the Zurek-Paz quantum chaos criterion of linear entropy growth and an information-geometric characterization of chaotic (integrable) energy level statistics of a quantum antiferromagnetic Ising spin chain in a tilted (transverse) external magnetic field are presented.Comment: 5 pages, presented at the 28th International Workshop on Bayesian Inference and Maximum Entropy Methods, Sao Paulo (Brazil)(July-2008

Testing Significance in Bayesian Classifiers.

The Fully Bayesian Significance Test (FBST) is a coherent Bayesian significance test for sharp hypotheses. This paper explores the FBST as a model selection tool for general mixture models, and gives some computational experiments for Multinomial-Dirichlet-Normal-Wishart models

Full Bayesian analysis for a class of jump-diffusion models

A new Bayesian significance test is adjusted for jump detection in a diffusion process. This is an advantageous procedure for temporal data having extreme valued outliers, like financial data, pluvial or tectonic forces records and others.Comment: 15 pages, 7 figures; real data analysis adde

FBST for Mixture Model Selection.

The Fully Bayesian Significance Test (FBST) is a coherent Bayesian significance test for sharp hypotheses. This paper proposes the FBST as a model selection tool for general mixture models, and compares its performance with Mclust, a model-based clustering software. The FBST robust performance strongly encourages further developments and investigations

Estratégia de Análise Quantitativa para Revisão de Pré-requisitos em uma Matriz Curricular do Curso de Bacharelado em Sistemas de Informação

Dentre as estratégias de gerenciamento de um curso superior de qualidade, a atualização da estrutura curricular e dos pré-requisitos entre disciplinas podem contribuir para o melhor preparo do aluno para os desafios da vida profissional, bem como diminuir as taxas de reprovação e evasão. Este artigo apresenta uma estratégia de análise quantitativa de associação entre disciplinas, visando a identificar potenciais necessidades de revisões nos pré-requisitos adotados no Projeto Pedagógico do Curso. Por ser baseada exclusivamente nos históricos acadêmicos dos estudantes, a estratégia é facilmente replicável em quaisquer cursos e universidades, e tem auxiliado na alteração de alguns pré-requisitos no curso de Bacharelado em Sistemas de Informação da USP

From Inference to Physics

Entropic dynamics, a program that aims at deriving the laws of physics from standard probabilistic and entropic rules for processing information, is developed further. We calculate the probability for an arbitrary path followed by a system as it moves from given initial to final states. For an appropriately chosen configuration space the path of maximum probability reproduces Newtonian dynamics.Comment: Presented at MaxEnt 2008, the 28th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (July 8-13, 2008, Boraceia Beach, Sao Paulo, Brazil

Gibbs Paradox and Similarity Principle

As no heat effect and mechanical work are observed, we have a simple experimental resolution of the Gibbs paradox: both the thermodynamic entropy of mixing and the Gibbs free energy change are zero during the formation of any ideal mixtures. Information loss is the driving force of these spontaneous processes. Information is defined as the amount of the compressed data. Information losses due to dynamic motion and static symmetric structure formation are defined as two kinds of entropies - dynamic entropy and static entropy, respectively. There are three laws of information theory, where the first and the second laws are analogs of the two thermodynamic laws. However, the third law of information theory is different: for a solid structure of perfect symmetry (e.g., a perfect crystal), the entropy (static entropy for solid state) S is the maximum. More generally, a similarity principle is set up: if all the other conditions remain constant, the higher the similarity among the components is, the higher the value of entropy of the mixture (for fluid phases) or the assemblage (for a static structure or a system of condensed phases) or any other structure (such as quantum states in quantum mechanics) will be, the more stable the mixture or the assemblage will be, and the more spontaneous the process leading to such a mixture or an assemblage or a chemical bond will be.Comment: Final version 12 pages, 10 figures, presented at MaxEnt200

FBST for a Generalized Poisson Distribution.

The Generalized Poisson Distribution (GPD) adds an extra parameter to the usual Poisson distribution. This parameter induces a loss of homogeneity in the stochastic processes modeled by the distribution. Thus, the generalized distribution becomes an useful model for counting processes where the occurrence of events is not homogeneous. This model creates the need for an inferential procedure, to test for the value of this extra parameter. The FBST (Full Bayesian Significance Test) is a Bayesian hypotheses test procedure, capable of providing an evidence measure on sharp hypotheses (where the dimension of the parametric space under the null hypotheses is smaller than that of the full parametric space). The goal of this work is study the empirical properties of the FBST for testing the nullity of extra parameter of the generalized Poisson distribution. Numerical experiments show a better performance of FBST with respect to the classical likelihood ratio test, and suggest that FBST is an efficient and robust tool for this application

Real Attribute Learning Algorithm.

This paper presents REAL, a Real-Valued Attribute Classification Tree Learning Algorithm. Several of the algorithm's unique features are explained by úe users' demands for a decision support tool to be used for evaluating financial operations strategies. Compared to competing algorithms, in our applications, REAL presents maj or advantages : (1) The REAL classification trees usually have smaller error rates. (2) A single conviction (or trust) measure at each leaf is more convenient than the traditional (probability, confidence-level) pair. (3) No need for an external pruning criterion

Full Bayesian Significance Test Applied to Multivariate Normal Structure Models

Abstract: The Pull Bayesian Significance Test (FBST) for precise hy- potheses is applied to a Multivariate Normal Structure (MNS) model. In the FBST we compute the evidence against the precise hypothesis. This evi- dence is the probability of the Highest Relative Surprise Set (HRSS) tangent to the sub-manifold (of the parameter space) that defines the null hypothesis. The MNS model we present appears when testing equivalence conditions for genetic expression measurements, using micro-array technology