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Neste trabalho discutimos a análise bayesiana em modelos TRI (Teoria da Resposta ao Item) de três parâmetros com respostas binárias e ordinais, considerando a ligação probito. Em ambos os casos usamos técnicas baseadas em MCCM (método de Monte Carlo baseado em Cadeias de Markov) para estimação dos parâmetros dos itens. No modelo com respostas binárias, consideramos dois conjuntos de dados resultantes de provas com itens de múltipla-escolha. Para esses dados, foi feito um estudo da sensibilidade à escolha de distribuições a priori, além de uma análise das estimativas a posteriori para os parâmetros dos itens: discriminação, dificuldade e probabilidade de acerto ao acaso. Um terceiro conjunto de dados foi utilizado no estudo do modelo com respostas ordinais. Estes dados são provenientes de uma disciplina básica de estatística, onde a prova contêm itens dissertativos. As respostas foram classificadas nas categorias: certa, errada ou parcialmente certa. Utilizamos o programa WinBugs para a estimação dos parâmetros do modelo binário e a função MCMCordfactanal do programa R para estimar os parâmetros do modelo ordinal. Ambos os softwares são não proprietários e gratuitos (livres). / In this dissertation the bayesian analysis for three parameters IRT (Item Response Theory) models with binaries and ordinals responses, considering the probit model, was discussed. For both cases, binary and ordinal, techniques based on MCCM (Monte Carlo Markov Chain) were used to estimate the items parameters. For binary response model, was considered two data sets from tests with multipla choices items. For these two data sets, a sensibility study of the priori distributions choice was considered, and also, an analyses of a posteriori estimates of the items parameters: discrimination, difficulties and guessing. A third data set is used to ilustrate the ordinal response model. This come from an elementar statistical course, where a test with open items is considered. The responses are classified in the following categories: correct, wrong or partial correct. The WinBugs software was used to estimate the parameters for the binary model and, for the ordinal model was considered the function MCMCordfactanal from R program.

Neste trabalho discutimos a análise bayesiana em modelos TRI (Teoria da Resposta ao Item) de três parâmetros com respostas binárias e ordinais, considerando a ligação probito. Em ambos os casos usamos técnicas baseadas em MCCM (método de Monte Carlo baseado em Cadeias de Markov) para estimação dos parâmetros dos itens. No modelo com respostas binárias, consideramos dois conjuntos de dados resultantes de provas com itens de múltipla-escolha. Para esses dados, foi feito um estudo da sensibilidade à escolha de distribuições a priori, além de uma análise das estimativas a posteriori para os parâmetros dos itens: discriminação, dificuldade e probabilidade de acerto ao acaso. Um terceiro conjunto de dados foi utilizado no estudo do modelo com respostas ordinais. Estes dados são provenientes de uma disciplina básica de estatística, onde a prova contêm itens dissertativos. As respostas foram classificadas nas categorias: certa, errada ou parcialmente certa. Utilizamos o programa WinBugs para a estimação dos parâmetros do modelo binário e a função MCMCordfactanal do programa R para estimar os parâmetros do modelo ordinal. Ambos os softwares são não proprietários e gratuitos (livres). / In this dissertation the bayesian analysis for three parameters IRT (Item Response Theory) models with binaries and ordinals responses, considering the probit model, was discussed. For both cases, binary and ordinal, techniques based on MCCM (Monte Carlo Markov Chain) were used to estimate the items parameters. For binary response model, was considered two data sets from tests with multipla choices items. For these two data sets, a sensibility study of the priori distributions choice was considered, and also, an analyses of a posteriori estimates of the items parameters: discrimination, difficulties and guessing. A third data set is used to ilustrate the ordinal response model. This come from an elementar statistical course, where a test with open items is considered. The responses are classified in the following categories: correct, wrong or partial correct. The WinBugs software was used to estimate the parameters for the binary model and, for the ordinal model was considered the function MCMCordfactanal from R program.

Thesis (Doctor of Engineering in Electrical Engineering)--Cape Peninsula University of Technology, 2017. / Remote sensing provides a way of frequently observing broad land surfaces. The availability of various earth observation data and their potential exploitation in a wide range of socioeconomic applications stimulated the rapid development of remote sensing technology. Much of the research and most of the publications dealing with remote sensing in the solar spectral domain focus on analysing and interpreting the spectral, spatial and temporal signatures of the observed areas. However, the angular signatures of the reflectance field, known as surface anisotropy, also merit attention. The current research took an exploratory approach to the land surface anisotropy described by the RPV model parameters derived from the MISR-HR processing system (denoted as MISR-HR anisotropy data or MISR-HR RPV data), over a period of 14+ years, for three typical terrestrial surfaces in the Western Cape Province of South Africa: a semi-desert area, a wheat field and a vineyard area. The objectives of this study were to explore (1) to what extent spectral and directional signatures of the MISR-HR RPV data may vary in time and space over the different targets (landscapes), and (2) whether the observed variations in anisotropy might be useful in classifying different land surfaces or as a supplementary method to the traditional land cover classification method. The objectives were achieved by exploring the statistics of the MISR-HR RPV data in each spectral band over the different land surfaces, as well as seasonality and trend in these data. The MISR-HR RPV products were affected by outliers and missing values, both of which influenced the statistics, seasonality and trend of the examined time series. This research proposes a new outlier detection method, based on the cost function derived from the RPV model inversion process. Removed outliers and missing values leave gaps in a MISR-HR RPV time series; to avoid introducing extra biases in the statistics of the anisotropy data, this research kept the gaps and relied on gap-resilient trend and seasonality detection methods, such as the Mann-Kendal trend detection and Lomb-Scargle periodogram methods. The exploration of the statistics of the anisotropy data showed that RPV parameter rho exhibited distinctive over the different study sites; NIR band parameter k exhibits prominent high values for the vineyard area; red band parameter Theta data are not that distinctive over different study sites; variance is important in describing all three RPV parameters. The explorations on trends also demonstrated interesting findings: the downward trend in green band parameter rho data for the semi-desert and vineyard areas; and the upward trend in blue band parameters k and Theta data for all the three study sites. The investigation on seasonality showed that all the RPV parameters had seasonal variations which differed over spectral bands and land covers; the results confirmed expectations in previous literature that parameter varies regularly along the observation time, and also revealed seasonal variations in the parameter rho and Theta data. The explorations on the statistics and seasonality of the MISR-HR anisotropy data show that these data are potentially useful for classifying different landscapes. Finally, the classification results demonstrated that both red band parameter rho data and NIR band parameter k data could successfully separate the three different land surfaces in this research, which fulfilled the second primary objective of this study. This research also demonstrated a classification method using multiple RPV parameters as the classification signatures to discriminate different terrestrial surfaces; significant separation results were obtained by this method.

A cathedral of the Church of England is the seat of the bishop and a centre of worship and mission. The history of this institution is followed from the English Reformation, which it survived, through to the Commonwealth, which it did not. Restored on the return of the monarchy, it then survived with little further trouble until the nineteenth century, when a lot of its income was diverted to the provision of churches and ministers for the populous urban and industrialised areas, which the Church could not fund in any other way. It was the subject of investigation by two Royal Commissions in the nineteenth century and three church-inspired commissions in the twentieth. These commissions stressed the links that should exist between cathedral, bishop and diocese, which the nineteenth century diocesan revival also encouraged, and suggested changes in instruments of governance to achieve this. Some proposals came to nothing, but others were brought into law. Unlike the Roman Catholic cathedral, the Anglican one never lost its autonomy. The religious situation in Britain today is considered in the light of some contemporary sociology and psychology, and it is recognised that the continued decline in the fortunes of the Church is tied up with the massive subjective turn which characterises contemporary culture. The cathedral has not shared the mistrust which faces the Church, and its various roles are discussed in the light of its continued hold on public affection. The conclusions reached are that, although the cathedral now has strong links with bishop and diocese, it should retain its independence within relationships of interdependence with them, to enable it to harness the popularity which it enjoys to remain a centre of worship, but primarily to concentrate on being a centre of mission. Appropriate ways of achieving that are discussed.