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Stochastic Nested Aggregation for Images and Random FieldsWesolkowski, Slawomir Bogumil 27 March 2007 (has links)
Image segmentation is a critical step in building a computer vision algorithm that is able to distinguish between separate objects in an image scene. Image segmentation is based on two fundamentally intertwined components: pixel comparison and pixel grouping. In the pixel comparison step, pixels are determined to be similar or different from each other. In pixel grouping, those pixels which are similar are grouped together to form meaningful regions which can later be processed. This thesis makes original contributions to both of those areas.
First, given a Markov Random Field framework, a Stochastic Nested Aggregation (SNA) framework for pixel and region grouping is presented and thoroughly analyzed using a Potts model. This framework is applicable in general to graph partitioning and discrete estimation problems where pairwise energy models are used. Nested aggregation reduces the computational complexity of stochastic algorithms such as Simulated Annealing to order O(N) while at the same time allowing local deterministic approaches such as Iterated Conditional Modes to escape most local minima in order to become a global deterministic optimization method. SNA is further enhanced by the introduction of a Graduated Models strategy which allows an optimization algorithm to converge to the model via several intermediary models. A well-known special case of Graduated Models is the Highest Confidence First algorithm which merges pixels or regions that give the highest global energy decrease. Finally, SNA allows us to use different models at different levels of coarseness. For coarser levels, a mean-based Potts model is introduced in order to compute region-to-region gradients based on the region mean and not edge gradients.
Second, we develop a probabilistic framework based on hypothesis testing in order to achieve color constancy in image segmentation. We develop three new shading invariant semi-metrics based on the Dichromatic Reflection Model. An RGB image is transformed into an R'G'B' highlight invariant space to remove any highlight components, and only the component representing color hue is preserved to remove shading effects. This transformation is applied successfully to one of the proposed distance measures. The probabilistic semi-metrics show similar performance to vector angle on images without saturated highlight pixels; however, for saturated regions, as well as very low intensity pixels, the probabilistic distance measures outperform vector angle.
Third, for interferometric Synthetic Aperture Radar image processing we apply the Potts model using SNA to the phase unwrapping problem. We devise a new distance measure for identifying phase discontinuities based on the minimum coherence of two adjacent pixels and their phase difference. As a comparison we use the probabilistic cost function of Carballo as a distance measure for our experiments.
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Dynamic Bayesian models for modelling environmental space-time fieldsDou, Yiping 05 1900 (has links)
This thesis addresses spatial interpolation and temporal prediction using air pollution data by several space-time modelling approaches. Firstly, we implement the dynamic linear modelling (DLM) approach in spatial interpolation and find various potential
problems with that approach. We develop software to implement our approach. Secondly, we implement a Bayesian spatial prediction (BSP) approach to model spatio-temporal ground-level ozone fields and compare the accuracy of that approach with that of the DLM. Thirdly, we develop a Bayesian version empirical orthogonal function (EOF) method to incorporate the uncertainties due to temporally varying spatial process, and the spatial variations at broad- and fine-
scale. Finally, we extend the BSP into the DLM framework to develop a unified Bayesian spatio-temporal model for univariate and
multivariate responses. The result generalizes a number of current approaches in this field.
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外匯選擇權的定價-馬可夫鏈蒙地卡羅法(MCMC)之績效探討任紀為 Unknown Date (has links)
在真實世界中,我們可以觀察到許多財務或經濟變數(股價、匯率、利率等)有時波動幅度非常微小,呈現相對穩定的狀態(Regime);有時會由於政治因素或經濟環境的變動,突然一段期間呈現瘋狂震盪的狀態。針對這種現象,已有學者提出狀態轉換波動度模型(Regime Switching Volatility Model,簡稱RSV)來捕捉此一現象。
本篇論文選擇每年交易金額非常龐大的外匯選擇權市場,以RSV模型為基礎,採用馬可夫鏈蒙地卡羅法 ( Markov Chain Monte Carlo,簡稱MCMC ) 中的吉普斯抽樣(Gibbs Sampling)法來估計RSV模型的參數,依此預測外匯選擇權在RSV模型下的價格。我們再將此價格與Black and Scholes(BS)法及實際市場交易的價格資料作比較,最後並提出笑狀波幅與隱含波動度平面的結果。結果顯示經由RSV模型與MCMC演算法所計算出來的選擇權價格確實優於傳統的BS方法,且能有效解釋波動率期間結構 (Volatility Term Structure) 與笑狀波幅 (Volatility Smile) 的現象,確實反應且捕捉到了市場上選擇權價格所應具備的特色。
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Dynamic Bayesian models for modelling environmental space-time fieldsDou, Yiping 05 1900 (has links)
This thesis addresses spatial interpolation and temporal prediction using air pollution data by several space-time modelling approaches. Firstly, we implement the dynamic linear modelling (DLM) approach in spatial interpolation and find various potential
problems with that approach. We develop software to implement our approach. Secondly, we implement a Bayesian spatial prediction (BSP) approach to model spatio-temporal ground-level ozone fields and compare the accuracy of that approach with that of the DLM. Thirdly, we develop a Bayesian version empirical orthogonal function (EOF) method to incorporate the uncertainties due to temporally varying spatial process, and the spatial variations at broad- and fine-
scale. Finally, we extend the BSP into the DLM framework to develop a unified Bayesian spatio-temporal model for univariate and
multivariate responses. The result generalizes a number of current approaches in this field.
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Estimativas de parâmetros genéticos de características reprodutivas de ovinos Santa Inês utilizando inferência BayesianaMontalván, Zoila Catalina Rabanal de 26 July 2013 (has links)
The objective of this study was to estimate the values of the (co)variance components and genetic parameters for reproductive traits in Santa Inês sheep raised in different states and registered with the Associação Sergipana dos Criadores de Caprinos e Ovinos (Goat and Sheep Breeders Association of Sergipe). The database used was provided by the Association and comprised pedigree observations originating from 11,483 registered individuals, out of which 2,238 were born in the program and had calving records, being part of the relationship matrix. After restriction application, there remained 843 animals for analysis which had data related to the age at first calving (IPP1), 151 with data related to the median calving interval (IPM2), and 151 to the interval between first and second calvings (IPS3). To obtain the values of (co)variance components and genetic parameters, we used the Bayesian inference under an animal model with the Gibbs sampling algorithm aided by the MTGSAM program. The two-trait linear model used considered the contemporary group as the fixed effect for IPP1, IPM2, and IPS3, and the type of calving and the age of the animal at calving as the covariate effect. The estimated values of h2 for IPP1, IPM2, and IPS3 were equal to 0.19 ± 0.0459, 0.0169 ± 0:36, and 0:35 ± 0.016 respectively. The estimated heritability for IPP1is considered average and the values for IPM2 and IPS3 were considered high, which leads to the conclusion that these characteristics can be used as selection criteria in a breeding program of Santa Inês sheep. The estimated value for the correlation between IPP1 and IPM2, and IPP1 and IP2, were negative and equal to rg12 = -0.2569 ± 0.0546, rg13= -0.1134 ± 0.0553, which were physiological expected low values that represent a trend which suggests individual selection for those traits. However, the rg23 shows a positive and high trend of 0.9601 ± 0.0091 for IPM2 and IPS3 suggesting indirect selection as the best option for these traits. / Objetivou-se estimar componentes de (co)variância e parâmetros genéticos para características reprodutivas de ovinos Santa Inês, criados em diferentes estados e registrados na Associação Sergipana dos Criadores de Caprinos e Ovinos. O banco de dados utilizado foi fornecido por esta associação, composto por observações de pedigree originadas de 11.483 indivíduos registrados dos quais 2.238 eram nascidos no programa e tinham registro de parto, permanecendo na matriz de parentesco. Após a aplicação das restrições, foram mantidas na análise 843 animais com dados referentes a característica idade ao primeiro parto (IPP1), 151 referentes a intervalo médio ao parto (IPM2) e 151 para intervalo entre primeiro e segundo parto (IPS3). Para obter os valores dos componentes de (co)variância e parâmetros genéticos utilizou-se analise bayesiana sob modelo animal mediante o algorismo Amostrador de Gibbs com o auxilio do programa MTGSAM. O modelo linear bicaracterística utilizado considerava como efeito fixo o grupo contemporâneo para as características IPP1, IPM2 e IPS3, considerou-se o efeito do tipo de parto e a idade do animal ao parto como efeito (co)variável. Os valores de h2 estimados para IPP1, IPM2 e IPS3 foram iguais a 0.19±0.0459, 0.36±0.0169 e 0.35±0.016 respectivamente. O valor estimado da herdabilidade para IPP1 é considerado médio e os valor para IPM2 e IPS3 alto, fato que leva a concluir que estas característica podem ser usadas como critério de seleção em um programa de melhoramento de ovinos da raça Santa Inês. O valor estimado para a correlação entre características IPP1 e IMP2; IPP1 e IPS2 foram negativos e iguais a rg12= -0.2569 ± 0.0546, rg13= -0.1134 ± 0.0553 valores fisiologicamente esperados de baixa magnitude que sugerem seleção individual para essas características, entretanto para IPM2 e IPS3 a rg23 mostra tendência positiva e muito elevada igual a rg23= 0.9601 ± 0.0091 valor que indica a seleção indireta o melhor caminho.
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Dynamic Bayesian models for modelling environmental space-time fieldsDou, Yiping 05 1900 (has links)
This thesis addresses spatial interpolation and temporal prediction using air pollution data by several space-time modelling approaches. Firstly, we implement the dynamic linear modelling (DLM) approach in spatial interpolation and find various potential
problems with that approach. We develop software to implement our approach. Secondly, we implement a Bayesian spatial prediction (BSP) approach to model spatio-temporal ground-level ozone fields and compare the accuracy of that approach with that of the DLM. Thirdly, we develop a Bayesian version empirical orthogonal function (EOF) method to incorporate the uncertainties due to temporally varying spatial process, and the spatial variations at broad- and fine-
scale. Finally, we extend the BSP into the DLM framework to develop a unified Bayesian spatio-temporal model for univariate and
multivariate responses. The result generalizes a number of current approaches in this field. / Science, Faculty of / Statistics, Department of / Graduate
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A Degradation-based Burn-in Optimization for Light Display Devices with Two-phase Degradation Patterns considering Warranty Durations and Measurement ErrorsGao, Yong January 2017 (has links)
No description available.
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Combining Subject Expert Experimental Data with Standard Data in Bayesian Mixture ModelingXiong, Hui 26 September 2011 (has links)
No description available.
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Performance, efficiency and complexity in multiple access large-scale MIMO Systems. / Desempenho, eficiência e complexidade de sistemas de comunicação MIMO denso de múltiplo acesso.Mussi, Alex Miyamoto 08 May 2019 (has links)
Systems with multiple transmitting and receiving antennas in large-scale (LS-MIMO - large-scale multipleinput multiple-output) enable high spectral and energy efficiency gains, which results in an increase in the data transmission rate in the same band, without increasing the transmitted power per user. In addition, with the increase of the number of antennas in the base station (BS) it is possible to attend to a larger number of users per cell, in the same occupied band. Furthermore, it has been found in the literature that the reported advantages of LS-MIMO systems can be obtained with a large number of antennas on at least one side of the communication, usually in BS due to physical restriction in user equipments. However, such advantages have their cost: the use of a large number of antennas also difficult tasks involving signal processing, such as estimation of channel coefficients, precoding and signal detection. It is at this juncture that this Doctoral Thesis is developed, in which the computational complexity of performing efficient detection methods in LSMIMO communication systems is explored through the analysis of algorithms and optimization techniques in the solution of specific problems and still open. More precisely, this Thesis discusses and proposes promising detection techniques in LS-MIMO systems, aiming to improve performance metrics - in terms of error rate - and computational complexity - in terms of the number of mathematical operations. Initially, the problem is introduced through a conventional MIMO system model, where channels with imperfect estimates and correlation between transmitter (Tx) and receiver (Rx) antennas are considered. Preprocessing techniques based on lattice reduction (LR) are applied in linear detectors, in addition to the sphere decoder (SD), which proposes a lookup table procedure in order to provide a reduction in computational complexity. It is shown that the LR method in the pre-detection results in a significant performance gain in both the condition of uncorrelated and correlated channels, and in the latter scenario the improvement is even more remarkable due to the diversity gain provided. On the other hand, the complexity involved in the application of LR in high correlation scenarios becomes preponderant in linear detectors. In the LR-SD using the lookup table procedure, the optimum gain was reached in all scenarios, as expected, and resulted in a lower complexity than maximum likelihood (ML) detector, even with maximum correlation between antennas, which represents the most complex scenario for the LR technique. Next, the message passing (MP) detector is investigated, which makes use of Markov random fields (MRF) and factor graph (FG) graphical models. Moreover, it is shown in the literature that the message damping (MD) method applied to the MRF detector brings relevant performance gain without increasing computational complexity. On the other hand, the DF value is specified for only a restricted range of scenarios. Numerical results are extensively generated, in order to obtain a range of analysis of the MRF with MD, which resulted in the proposition of an optimal value for the DF, based on numerical curve fitting. Finally, in the face of the MGS detector, two approaches are proposed to reduce the negative impact caused by the random solution when high modulation orders are employed. The first is based on an average between multiple samples, called aMGS (averaged MGS). The second approach deploys a direct restriction on the range of the random solution, limiting in d the neighborhood of symbols that can be sorted, being called d-sMGS. Numerical simulation results show that both approaches result in gain of convergence in relation to MGS, especially: in regions of high system loading, d-sMGS detection demonstrated significant gain in both performance and complexity compared to aMGS and MGS; although in low-medium loading, the aMGS strategy showed less complexity, with performance marginally similar to the others. Furthermore, it is concluded that increasing the dimensions of the system favors a smaller restriction in the neighborhood. / Sistemas com múltiplas antenas transmissoras e múltiplas antenas receptoras em larga escala (LS-MIMO - large-scale multiple-input multiple-output) possibilitam altos ganhos em eficiência espectral e energética, o que resulta em aumento da taxa de transmissão de dados numa mesma banda ocupada, sem acréscimo da potência transmitida por usuário. Além disso, com o aumento do número de antenas na estação rádio-base (BS- base station) possibilita-se o atendimento de maior número de usuários por célula, em uma mesma banda ocupada. Ademais, comprovou-se na literatura que as vantagens relatadas dos sistemas LS-MIMO podem ser obtidas com um grande número de antenas em, pelo menos, um dos lados da comunicação, geralmente na BS devido à restrição física nos dispositivos móveis. Contudo, tais vantagens têm seu custo: a utilização de um grande número de antenas também dificulta tarefas que envolvem processamento de sinais, como estimação dos coeficientes de canal, precodificação e detecção de sinais. É nessa conjuntura em que se desenvolve esta Tese de Doutorado, na qual se explora o compromisso desempenho versus complexidade computacional de métodos eficientes de detecção em sistemas de comunicações LS-MIMO através da análise de algoritmos e técnicas de otimização na solução de problemas específicos e ainda em aberto. Mais precisamente, a presente Tese discute e propõe técnicas promissoras de detecção em sistemas LS-MIMO, visando a melhoria de métricas de desempenho - em termos de taxa de erro - e complexidade computacional - em termos de quantidade de operações matemáticas. Inicialmente, o problema é introduzido através de um modelo de sistema MIMO convencional, em que são considerados canais com estimativas imperfeitas e com correlação entre as antenas transmissoras (Tx) e entre as receptoras (Rx). Aplicam-se técnicas de pré-processamanto baseadas na redução treliça (LR - lattice reduction) em detectores lineares, além do detector esférico (SD - sphere decoder), o qual é proposto um procedimento de tabela de pesquisa a fim de prover redução na complexidade computacional. Mostra-se que o método LR na pré-detecção resulta em ganho de desempenho significante tanto na condição de canais descorrelacionados quanto fortemente correlacionados, sendo que, neste último cenário a melhoria é ainda mais notável, devido ao ganho de diversidade proporcionado. Por outro lado, a complexidade envolvida na aplicação da LR em alta correlação torna-se preponderante em detectores lineares. No LR-SD utilizando o procedimento de tabela de pesquisa, o ganho ótimo foi alcançado em todos os cenários, como esperado, e resultou em complexidade inferior ao detector de máxima verossimilhança (ML - maximum likelihood), mesmo com máxima correlação entre antenas, a qual representa o cenário de maior complexidade a técnica LR. Em seguida, o detector por troca de mensagens (MP - message passing) é investigado, o qual faz uso de modelos grafos do tipo MRF (Markov random fields) e FG (factor graph). Além disso, mostra-se na literatura que o método de amortecimento de mensagens (MD - message damping) aplicado ao detector MRF traz relevante ganho de desempenho sem aumento na complexidade computacional. Por outro lado, o valor do DF (damping factor) é especificado para somente uma variedade restrita de cenários. Resultados numéricos são extensivamente gerados, de forma a dispor de uma gama de análises de comportamento do MRF com MD, resultando na proposição de um valor ótimo para o DF, baseando-se em ajuste de curva numérico. Finalmente, em face ao detector MGS (mixed Gibbs sampling), são propostas duas abordagens visando a redução do impacto negativo causado pela solução aleatória quando altas ordens de modulação são empregadas. A primeira é baseada em uma média entre múltiplas amostras, chamada aMGS (averaged MGS). A segunda abordagem realiza uma restrição direta no alcance da solução aleatória, limitando em até d a vizinhança de símbolos que podem ser sorteados, sendo chamada de d-sMGS (d-simplificado MGS). Resultados de simulação numérica demonstram que ambas abordagens resultam em ganho de convergência em relação ao MGS, destacando-se: em regiões de alto carregamento, a detecção d-sMGS demonstrou ganho expressivo tanto em desempenho quanto em complexidade se comparada à aMGS e MGS; já em baixo-médio carregamentos, a estratégia aMGS demonstrou menor complexidade, com desempenho marginalmente semelhante às demais. Além disso, conclui-se que o aumento do número de dimensões do sistema favorece uma menor restrição na vizinhança.
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利用預測分析-篩選及檢視再保險契約中之承保風險 / Selecting and Monitoring Insurance Risk on Reinsurance Treaties Using Predictive Analysis吳家安, Wu, Chiao-An Unknown Date (has links)
傳統的保險人在面對保險契約所承保的風險時,常會藉由國際上的再保險市場來分散其保險風險。由於所承保險事件的不確定性,保險人需要謹慎小心評估其保險風險並將承保風險轉移至再保險人。再保險有兩種主要的保險型式,可區分成比例再保契約及超額損失再保契約,保險人將利用這些再保險契約來分散求償給付時的損失,加強保險人本身的財務清償能力。
本研究,主要在於建構未來損失求償幅度或頻率的預測分佈並模擬未來支付求償的損失。簡單重點重複抽樣法是一種從危險參數的驗後分佈中抽樣的抽樣方法。然而,蒙地卡羅模擬是一種利用大量電腦運算計算近似預測分佈的逼近方法。利用被選取危險參數的驗前分佈來模擬其驗後分佈,並建構可能的承保危險參數結構,將基於馬可夫鏈蒙地卡羅理論的吉普生抽樣方法決定最適自留額,同時運用於再保險合約決策擬定過程。
最後,考慮於不同的再保險契約下來衡量再保險人的自負財務風險。基本上我們研究的對象是針對保險人所承保的風險,再藉由上述的方法來模擬、近似以量化所衍生的財務風險。這將有助於保險人清楚地瞭解其承保的風險,並對其承保業務做妥善的財務風險管理。本研究提供保險人具體的模型建構方法並對此建構技巧做詳細說明及實證分析。 / Insurers traditionally transfer their insurance risk through the international reinsurance market. Due to the uncertainty of these insured risks, the primary insurer need to carefully evaluate the insured risk and further transfer these risks to his ceding reinsurers. There are two major types of reinsurance, i.e. pro rata treaty and excess of loss treaty, used in protecting the claim losses.
In this article, the predictive distribution of the claim size is constructed to monitor the future claim underwriting losses based on the reinsurance agreement. Simple Importance Resampling (SIR) are employed in sampling the posterior distribution of risk parameters. Then Monte Carlo simulations are used to approximate the predictive distribution. Plausible prior distributions of these risk parameters are chosen in simulation its posterior distribution. Markov chain Monte Carlo (MCMC) method using Gibbs sampling scheme is also performed based on possible parametric structures. Both the pro rata and excess of loss treaties are investigated to quantify the retention risks of the ceding reinsurers.
The insurance risks are focused in our model. Through the implemented model and simulation techniques, it is beneficial for the primary insurer in projecting his underwriting risks. The results show a significant advantage and flexibility using this approach in risk management. This article outlines the procedure of building the model. Finally a practical case study is performed for numerical illustrated.
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