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.

Fitting genetic models to twin data with binary and ordered categorical responses: A comparison of structural equation modelling and Bayesian hierarchical models

We compare Bayesian methodology utilizing free-ware BUGS (Bayesian Inference Using Gibbs Sampling) with the traditional structural equation modelling approach based on another free-ware package, Mx. Dichotomous and ordinal (three category) twin data were simulated according to different additive genetic and common environment models for phenotypic variation. Practical issues are discussed in using Gibbs sampling as implemented by BUGS to fit subject-specific Bayesian generalized linear models, where the components of variation may be estimated directly. The simulation study (based on 2000 twin pairs) indicated that there is a consistent advantage in using the Bayesian method to detect a correct model under certain specifications of additive genetics and common environmental effects. For binary data, both methods had difficulty in detecting the correct model when the additive genetic effect was low (between 10 and 20%) or of moderate range (between 20 and 40%). Furthermore, neither method could adequately detect a correct model that included a modest common environmental effect (20%) even when the additive genetic effect was large (50%). Power was significantly improved with ordinal data for most scenarios, except for the case of low heritability under a true ACE model. We illustrate and compare both methods using data from 1239 twin pairs over the age of 50 years, who were registered with the Australian National Health and Medical Research Council Twin Registry (ATR) and presented symptoms associated with osteoarthritis occurring in joints of the hand.

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).

Prognostic modelling of breast cancer patients: a benchmark of predictive models with external validation

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Electrotécnica e de Computadores – Sistemas Digitais e Percepcionais pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

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.

Separating sequential equilibrium in a Bayesian international duopoly

We consider two Cournot firms, one located in the home country and the other in the foreign country, producing substitute goods for consumption in a third country. We suppose that neither the home government nor the foreign firm know the costs of the home firm, while the foreign firm cost is common knowledge. We determine the separating sequential equilibrium outputs.

Spatio-temporal patterns of tuberculosis incidence in Ribeirão Preto, State of São Paulo, southeast Brazil, and their relationship with social vulnerability: a Bayesian analysis

INTRODUCTION: The purpose of this ecological study was to evaluate the urban spatial and temporal distribution of tuberculosis (TB) in Ribeirão Preto, State of São Paulo, southeast Brazil, between 2006 and 2009 and to evaluate its relationship with factors of social vulnerability such as income and education level. METHODS: We evaluated data from TBWeb, an electronic notification system for TB cases. Measures of social vulnerability were obtained from the SEADE Foundation, and information about the number of inhabitants, education and income of the households were obtained from Brazilian Institute of Geography and Statistics. Statistical analyses were conducted by a Bayesian regression model assuming a Poisson distribution for the observed new cases of TB in each area. A conditional autoregressive structure was used for the spatial covariance structure. RESULTS: The Bayesian model confirmed the spatial heterogeneity of TB distribution in Ribeirão Preto, identifying areas with elevated risk and the effects of social vulnerability on the disease. We demonstrated that the rate of TB was correlated with the measures of income, education and social vulnerability. However, we observed areas with low vulnerability and high education and income, but with high estimated TB rates. CONCLUSIONS: The study identified areas with different risks for TB, given that the public health system deals with the characteristics of each region individually and prioritizes those that present a higher propensity to risk of TB. Complex relationships may exist between TB incidence and a wide range of environmental and intrinsic factors, which need to be studied in future research.

A bayesian approach for NDT data fusion: The Saint Torcato Church case study

This paper presents a methodology based on the Bayesian data fusion techniques applied to non-destructive and destructive tests for the structural assessment of historical constructions. The aim of the methodology is to reduce the uncertainties of the parameter estimation. The Young's modulus of granite stones was chosen as an example for the present paper. The methodology considers several levels of uncertainty since the parameters of interest are considered random variables with random moments. A new concept of Trust Factor was introduced to affect the uncertainty related to each test results, translated by their standard deviation, depending on the higher or lower reliability of each test to predict a certain parameter.

Generalized factor models: a bayesian approach

There is recent interest in the generalization of classical factor models in which the idiosyncratic factors are assumed to be orthogonal and there are identification restrictions on cross-sectional and time dimensions. In this study, we describe and implement a Bayesian approach to generalized factor models. A flexible framework is developed to determine the variations attributed to common and idiosyncratic factors. We also propose a unique methodology to select the (generalized) factor model that best fits a given set of data. Applying the proposed methodology to the simulated data and the foreign exchange rate data, we provide a comparative analysis between the classical and generalized factor models. We find that when there is a shift from classical to generalized, there are significant changes in the estimates of the structures of the covariance and correlation matrices while there are less dramatic changes in the estimates of the factor loadings and the variation attributed to common factors.

Hydrogeophysical data integration at larger scales: Application of Bayesian sequential simulation for the characterization of heterogeneous alluvial aquifers

Knowledge of the spatial distribution of hydraulic conductivity (K) within an aquifer is critical for reliable predictions of solute transport and the development of effective groundwater management and/or remediation strategies. While core analyses and hydraulic logging can provide highly detailed information, such information is inherently localized around boreholes that tend to be sparsely distributed throughout the aquifer volume. Conversely, larger-scale hydraulic experiments like pumping and tracer tests provide relatively low-resolution estimates of K in the investigated subsurface region. As a result, traditional hydrogeological measurement techniques contain a gap in terms of spatial resolution and coverage, and they are often alone inadequate for characterizing heterogeneous aquifers. Geophysical methods have the potential to bridge this gap. The recent increased interest in the application of geophysical methods to hydrogeological problems is clearly evidenced by the formation and rapid growth of the domain of hydrogeophysics over the past decade (e.g., Rubin and Hubbard, 2005).

Bayesian Inference in the Time Varying Cointegration Model

There are both theoretical and empirical reasons for believing that the parameters of macroeconomic models may vary over time. However, work with time-varying parameter models has largely involved Vector autoregressions (VARs), ignoring cointegration. This is despite the fact that cointegration plays an important role in informing macroeconomists on a range of issues. In this paper we develop time varying parameter models which permit cointegration. Time-varying parameter VARs (TVP-VARs) typically use state space representations to model the evolution of parameters. In this paper, we show that it is not sensible to use straightforward extensions of TVP-VARs when allowing for cointegration. Instead we develop a specification which allows for the cointegrating space to evolve over time in a manner comparable to the random walk variation used with TVP-VARs. The properties of our approach are investigated before developing a method of posterior simulation. We use our methods in an empirical investigation involving a permanent/transitory variance decomposition for inflation.