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Search Results: 1 - 10 of 2577 matches for " Adriano Polpo "
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Reliability Estimators for the Components of Series and Parallel Systems: The Weibull Model  [PDF]
Felipe L. Bhering, Carlos A. de B. Pereira, Adriano Polpo
Applied Mathematics (AM) , 2014, DOI: 10.4236/am.2014.511157

This paper presents a hierarchical Bayesian approach to the estimation of components’ reliability (survival) using a Weibull model for each of them. The proposed method can be used to estimation with general survival censored data, because the estimation of a component’s reliability in a series (parallel) system is equivalent to the estimation of its survival function with right- (left-) censored data. Besides the Weibull parametric model for reliability data, independent gamma distributions are considered at the first hierarchical level for the Weibull parameters and independent uniform distributions over the real line as priors for the parameters of the gammas. In order to evaluate the model, an example and a simulation study are discussed.

On relative weighted entropies with central moments weight functions
Salimeh Yasaei Sekeh,Adriano Polpo
Mathematics , 2015,
Abstract: Following [1], the aim of this paper is to analyze the relative weighted entropy involving the central moments weight functions. We compare the standard relative entropy with the weighted case in two particular forms of Gaussian distributions. As an application, the weighted deviance information criterion is proposed.
Confidence Statements for Ordering Quantiles
Carlos A. de B. Pereira,Cassio P. de Campos,Adriano Polpo
Statistics , 2012,
Abstract: This work proposes Quor, a simple yet effective nonparametric method to compare independent samples with respect to corresponding quantiles of their populations. The method is solely based on the order statistics of the samples, and independence is its only requirement. All computations are performed using exact distributions with no need for any asymptotic considerations, and yet can be run using a fast quadratic-time dynamic programming idea. Computational performance is essential in high-dimensional domains, such as gene expression data. We describe the approach and discuss on the most important assumptions, building a parallel with assumptions and properties of widely used techniques for the same problem. Experiments using real data from biomedical studies are performed to empirically compare Quor and other methods in a classification task over a selection of high-dimensional data sets.
Chromosomal Damage and Apoptosis in Exfoliated Buccal Cells from Individuals with Oral Cancer
Lavínia Tércia Magalh es Dórea,José Roberto Cardoso Meireles,Júlia Paula Ramos Lessa,Márcio Campos Oliveira,Carlos Alberto de Bragan a Pereira,Adriano Polpo de Campos,Eneida de Moraes Macílio Cerqueira
International Journal of Dentistry , 2012, DOI: 10.1155/2012/457054
Abstract: This study aimed to investigate cytological abnormalities indicative of chromosome damage (micronuclei) and apoptosis (karyorrhexis, pyknosis, and condensed chromatin) in exfoliated cells from the buccal mucosa of patients with oral cancer and control subjects. The sample included twenty individuals with oral cancer and forty individuals with normal buccal mucosa. Material was collected from the cheek epithelium in areas with lesions and areas without abnormalities. A minimum of one thousand cells was analyzed. Micronuclei were found significantly more frequently in cells collected from lesions than in cells from normal areas, independent of the presence/absence of cancer (<0.0001). They were also significantly more frequent in smokers and in mouthwash users (<0.0001). Apoptosis occurred significantly less frequently in individuals with oral cancer (<0.0001). These results show that oral cancer is associated with higher frequency of chromosomal damage and suggest that apoptosis is compromised in the buccal cells of individuals with this kind of neoplasia.
Strategy Selection in Influence Diagrams using Imprecise Probabilities
Cassio Polpo de Campos,Qiang Ji
Computer Science , 2012,
Abstract: This paper describes a new algorithm to solve the decision making problem in Influence Diagrams based on algorithms for credal networks. Decision nodes are associated to imprecise probability distributions and a reformulation is introduced that finds the global maximum strategy with respect to the expected utility. We work with Limited Memory Influence Diagrams, which generalize most Influence Diagram proposals and handle simultaneous decisions. Besides the global optimum method, we explore an anytime approximate solution with a guaranteed maximum error and show that imprecise probabilities are handled in a straightforward way. Complexity issues and experiments with random diagrams and an effects-based military planning problem are discussed.
Belief Updating and Learning in Semi-Qualitative Probabilistic Networks
Cassio Polpo de Campos,Fabio Gagliardi Cozman
Computer Science , 2012,
Abstract: This paper explores semi-qualitative probabilistic networks (SQPNs) that combine numeric and qualitative information. We first show that exact inferences with SQPNs are NPPP-Complete. We then show that existing qualitative relations in SQPNs (plus probabilistic logic and imprecise assessments) can be dealt effectively through multilinear programming. We then discuss learning: we consider a maximum likelihood method that generates point estimates given a SQPN and empirical data, and we describe a Bayesian-minded method that employs the Imprecise Dirichlet Model to generate set-valued estimates.
Time-integrated North Atlantic Oscillation as a proxy for climatic change  [PDF]
Adriano Mazzarella
Natural Science (NS) , 2013, DOI: 10.4236/ns.2013.51A023

The time-integrated yearly values of North Atlantic Oscillation (INAO) are found to be well correlated to the sea surface temperature. The results give the feasibility of using INAO as a good proxy for climate change and contribute to a more complete picture of the full range of variability inherent in the climate system. Moreover, the extrapolation in the future of the well identified 65-year harmonic in INAO suggests a gradual decline in global warming starting from 2005.

Solving Limited Memory Influence Diagrams
Denis Deratani Mauá,Cassio Polpo de Campos,Marco Zaffalon
Computer Science , 2011,
Abstract: We present a new algorithm for exactly solving decision making problems represented as influence diagrams. We do not require the usual assumptions of no forgetting and regularity; this allows us to solve problems with simultaneous decisions and limited information. The algorithm is empirically shown to outperform a state-of-the-art algorithm on randomly generated problems of up to 150 variables and $10^{64}$ solutions. We show that the problem is NP-hard even if the underlying graph structure of the problem has small treewidth and the variables take on a bounded number of states, but that a fully polynomial time approximation scheme exists for these cases. Moreover, we show that the bound on the number of states is a necessary condition for any efficient approximation scheme.
The Complexity of Approximately Solving Influence Diagrams
Denis D. Maua,Cassio Polpo de Campos,Marco Zaffalon
Computer Science , 2012,
Abstract: Influence diagrams allow for intuitive and yet precise description of complex situations involving decision making under uncertainty. Unfortunately, most of the problems described by influence diagrams are hard to solve. In this paper we discuss the complexity of approximately solving influence diagrams. We do not assume no-forgetting or regularity, which makes the class of problems we address very broad. Remarkably, we show that when both the tree-width and the cardinality of the variables are bounded the problem admits a fully polynomial-time approximation scheme.
Inference in Polytrees with Sets of Probabilities
Jose Carlos Ferreira da Rocha,Fabio Gagliardi Cozman,Cassio Polpo de Campos
Computer Science , 2012,
Abstract: Inferences in directed acyclic graphs associated with probability sets and probability intervals are NP-hard, even for polytrees. In this paper we focus on such inferences, and propose: 1) a substantial improvement on Tessems A / R algorithm FOR polytrees WITH probability intervals; 2) a new algorithm FOR direction - based local search(IN sets OF probability) that improves ON existing methods; 3) a collection OF branch - AND - bound algorithms that combine the previous techniques.The first two techniques lead TO approximate solutions, WHILE branch - AND - bound procedures can produce either exact OR approximate solutions.We report ON dramatic improvements ON existing techniques FOR inference WITH probability sets AND intervals, IN SOME cases reducing the computational effort BY many orders OF magnitude.
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