A Comparison of Bayesian and Sampling Theory Inferences in a Probit Model

HE PROBIT MODEL IS A POPULAR DEVICE for explaining binary choice decisions in econometrics. It has been used to describe choices such as labor force participation, travel mode, home ownership, and type of education. These and many more examples can be found in papers by Amemiya (1981) and Maddala (1983). Given the contribution of economics towards explaining such choices, and given the nature of data that are collected, prior information on the relationship between a choice probability and several explanatory variables frequently exists. Bayesian inference is a convenient vehicle for including such prior information. Given the increasing popularity of Bayesian inference it is useful to ask whether inferences from a probit model are sensitive to a choice between Bayesian and sampling theory techniques. Of interest is the sensitivity of inference on coefficients, probabilities, and elasticities. We consider these issues in a model designed to explain choice between fixed and variable interest rate mortgages. Two Bayesian priors are employed: a uniform prior on the coefficients, designed to be noninformative for the coefficients, and an inequality restricted prior on the signs of the coefficients. We often know, a priori, whether increasing the value of a particular explanatory variable will have a positive or negative effect on a choice probability. This knowledge can be captured by using a prior probability density function (pdf) that is truncated to be positive or negative. Thus, three sets of results are compared:those from maximum likelihood (ML) estimation, those from Bayesian estimation with an unrestricted uniform prior on the coefficients, and those from Bayesian estimation with a uniform prior truncated to accommodate inequality restrictions on the coefficients.

Non-invasive detection of aortic and coronary atherosclerosis in homozygous familial hypercholesterolemia by 64 slice multi-detector row computed tomography angiography

Homozygous familial hypercholesterolemia (HoFH) is a rare disorder characterized by the early onset of atherosclerosis, often at the ostia of coronary arteries. In this study we document for the first time that aortic and coronary atherosclerosis can be detected using 64 slice multiple detector row computed tomographic coronary angiography (CTCA). We studied five HoFH patients (three females, two males, mean age 19.8 +/- 2.9 years, age range 15-23 years, with a mean low density lipoprotein (LDL) cholesterol 618 +/- 211 mg/dL) using 64 slice CTCA. None of the patients showed evidence of ischemia with standard exercise testing. Calcified and mixed atherosclerotic plaques adjacent to or compromising the coronary artery ostia were found in all study subjects. Coronary plaques causing significant obstruction were found in one patient, who had previously undergone coronary artery bypass surgery and aortic valve replacement. Two other patients were noted to have non-obstructive calcified, mixed and non-calcified coronary artery plaques. Our data suggest that CTCA could be a useful non-invasive method for detection of early aortic and coronary atherosclerosis specifically affecting the coronary ostia in HoFH subjects. (c) 2007 Elsevier Ireland Ltd. All rights reserved.

Bayesian feedback versus Markovian feedback in a two-level atom

We compare two different approaches to the control of the dynamics of a continuously monitored open quantum system. The first is Markovian feedback, as introduced in quantum optics by Wiseman and Milburn [Phys. Rev. Lett. 70, 548 (1993)]. The second is feedback based on an estimate of the system state, developed recently by Doherty and Jacobs [Phys. Rev. A 60, 2700 (1999)]. Here we choose to call it, for brevity, Bayesian feedback. For systems with nonlinear dynamics, we expect these two methods of feedback control to give markedly different results. The simplest possible nonlinear system is a driven and damped two-level atom, so we choose this as our model system. The monitoring is taken to be homodyne detection of the atomic fluorescence, and the control is by modulating the driving. The aim of the feedback in both cases is to stabilize the internal state of the atom as close as possible to an arbitrarily chosen pure state, in the presence of inefficient detection and other forms of decoherence. Our results (obtained without recourse to stochastic simulations) prove that Bayesian feedback is never inferior, and is usually superior, to Markovian feedback. However, it would be far more difficult to implement than Markovian feedback and it loses its superiority when obvious simplifying approximations are made. It is thus not clear which form of feedback would be better in the face of inevitable experimental imperfections.

Are Ichthyosporea animals or fungi? Bayesian phylogenetic analysis of elongation factor 1α of Ichthyophonus irregularis

Ichthyosporea is a recently recognized group of morphologically simple eukaryotes, many of which cause disease in aquatic organisms. Ribosomal RNA sequence analyses place Ichthyosporea near the divergence of the animal and fungal lineages, but do not allow resolution of its exact phylogenetic position. Some of the best evidence for a specific grouping of animals and fungi (Opisthokonta) has come from elongation factor 1alpha, not only phylogenetic analysis of sequences but also the presence or absence of short insertions and deletions. We sequenced the EF-1alpha gene from the ichthyosporean parasite Ichthyophonus irregularis and determined its phylogenetic position using neighbor-joining, parsimony and Bayesian methods. We also sequenced EF-1alpha genes from four chytrids to provide broader representation within fungi. Sequence analyses and the presence of a characteristic 12 amino acid insertion strongly indicate that I. irregularis is a member of Opisthokonta, but do not resolve whether I. irregularis is a specific relative of animals or of fungi. However, the EF-1alpha of I. irregularis exhibits a two amino acid deletion heretofore reported only among fungi. (C) 2003 Elsevier Science (USA). All rights reserved.

Padrões espaço-temporais da mortalidade por câncer de mama em mulheres no Estado do Espírito Santo - um estudo ecológico

O câncer de mama é a principal neoplasia maligna que acomete o sexo feminino no Brasil. O câncer de mama é hoje uma doença de extrema importância para a saúde pública nacional, motivando ampla discussão em torno das medidas que promova o seu diagnóstico precoce, a redução em sua morbidade e mortalidade. A presente pesquisa possui três objetivos, cujos resultados encontram-se organizados em artigos. O primeiro objetivo buscou analisar a completude dos dados do Sistema de Informação de Mortalidade sobre os óbitos por câncer de mama em mulheres no Espírito Santo, Sudeste e Brasil (1998 a 2007). Realizou-se um estudo descritivo analítico baseado em dados secundários, onde foi analisado o número absoluto e percentual de não preenchimento das variáveis nas declarações de óbitos. Adotou-se escore para avaliar os graus de não completude. Os resultados para as variáveis sexo e idade foram excelentes tanto para o Espírito Santo, Sudeste e Brasil. O preenchimento das variáveis raça/cor, grau de escolaridade e estado civil apresentam problemas no Espírito Santo. Enquanto no Sudeste e Brasil as variáveis raça/cor e escolaridade têm tendência decrescente para a não completude, no Espírito Santo a tendência se mantém estável. Para a variável estado civil, a não completude tem tendência crescente no Estado do Espírito Santo. O segundo objetivo foi analisar a evolução das taxas de mortalidade por câncer de mama, em mulheres no Espírito Santo no período de 1980 a 2007. Estudo de série temporal, cujos dados sobre óbitos foram obtidos do Sistema de Informação de Mortalidade e as estimativas populacionais segundo idade e anos-calendário, do Instituto Brasileiro Geografia e Estatística. Os coeficientes específicos 9 de mortalidade, segundo faixa etária, foram calculados anualmente. A análise de tendência foi realizada por meio da padronização das taxas de mortalidade pelo método direto, em que a população do senso IBGE-2000, foi considerada padrão. No período de estudo, ocorreram 2.736 óbitos por câncer de mama. O coeficiente de mortalidade neste período variou de 3,41 a 10,99 por 100.000 mulheres. Os resultados indicam que há tendência de mortalidade por câncer de mama ao longo da série (p=0,001 com crescimento de 75,42%). Todas as faixas etárias a partir de 30 anos apresentaram tendência de crescimento da mortalidade estatisticamente significante (p=0,001). Os percentuais de crescimento foram aumentando, segundo as idades mais avançadas, sendo 48,4% na faixa de 40 a 49 anos, chegando a 92,3%, na faixa de 80 anos e mais. O terceiro objetivo foi realizar a análise espacial dos óbitos em mulheres por câncer de mama no estado do Espírito Santo, nos anos de 2003 a 2007, com análise das correlações espaciais dessa mortalidade e componentes do município. O cenário foi o Estado do Espírito Santo, composto por 78 municípios. Para análise dos dados, utilizou-se a abordagem bayesiana (métodos EBest Global e EBest Local) para correção de taxas epidemiológicas. Calculou-se o índice I de Moran, para dependência espacial em nível global e a estatística Moran Local. As maiores taxas estão concentradas em 19 municípios pertencentes às Microrregiões: Metropolitana (Fundão, Vitória, Vila Velha, Viana, Cariacica e Guarapari), Metrópole Expandida Sul (Anchieta, Alfredo Chaves), Pólo Cachoeiro (Vargem Alta, Rio Novo do Sul, Mimoso do Sul, Cachoeiro de Itapemirim, Castelo, Jerônimo Monteiro, Bom Jesus do Norte, Apiacá e Muqui) e Caparaó (Alegre e São José do Calçado). Os resultados da Estimação Bayesiana (Índice de Moran) dos óbitos por câncer de mama em mulheres ocorridos no estado do Espírito Santo, segundo os dados brutos e 10 ajustados indicam a existência de correlação espacial significativa para o mapa Local (I = 0,573; p = 0,001) e Global (I = 0,118; p = 0,039). Os dados brutos não apresentam correlação espacial (I = 0,075; p = 0,142).

Fluorescence Microscopy Imaging Denoising with Log-Euclidean Priors and Photobleaching Compensation

Fluorescent protein microscopy imaging is nowadays one of the most important tools in biomedical research. However, the resulting images present a low signal to noise ratio and a time intensity decay due to the photobleaching effect. This phenomenon is a consequence of the decreasing on the radiation emission efficiency of the tagging protein. This occurs because the fluorophore permanently loses its ability to fluoresce, due to photochemical reactions induced by the incident light. The Poisson multiplicative noise that corrupts these images, in addition with its quality degradation due to photobleaching, make long time biological observation processes very difficult. In this paper a denoising algorithm for Poisson data, where the photobleaching effect is explicitly taken into account, is described. The algorithm is designed in a Bayesian framework where the data fidelity term models the Poisson noise generation process as well as the exponential intensity decay caused by the photobleaching. The prior term is conceived with Gibbs priors and log-Euclidean potential functions, suitable to cope with the positivity constrained nature of the parameters to be estimated. Monte Carlo tests with synthetic data are presented to characterize the performance of the algorithm. One example with real data is included to illustrate its application.

16ª Conferência Internacional Metropolis nos Açores : as ilhas na centralidade da discussão

No presente editorial, a autora faz o enquadramento da Conferência Internacional Metropolis, que decorreu nos Açores, enaltecendo o facto de este evento ter decorrido, pela primeira vez, nas ilhas atlânticas.

Reinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Support

This paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to electricity market negotiating players. ALBidS uses a set of different strategies for providing decision support to market players. These strategies are used accordingly to their probability of success for each different context. The approach proposed in this paper uses a Bayesian network for deciding the most probably successful action at each time, depending on past events. The performance of the proposed methodology is tested using electricity market simulations in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). MASCEM provides the means for simulating a real electricity market environment, based on real data from real electricity market operators.