Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 86 for “"prior distribution"”.
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Constructing an Informative Prior Distribution of Noises in Seasonal Adjustment
Time series data is very common in our daily life. Since they are related to time, most of them show a periodicity. The existence of this periodic in uence leads to our research problem, seasonal adjustment. Seasonal adjustment is generally applied around us, especially in areas of economy and …
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Bayesian Time Series Structure Learning: Formulation of an Event Driven Prior Distribution
We study the prior distribution over structures of a Bayesian time series structure learning model—the Temporal Interaction Model (TIM) of Siracusa and Fisher III. We develop a new method for setting the hyperparameters of the TIM structure prior. Our contribution enables more consistent inference …
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Análisis de sensibilidad bayesiana a través de clases de distribuciones a priori: teoría y aplicaciones
… Bayesian Statistics obtain the posterior distribution of an underlying univariate or multivariate parameter as a consequence of the likelihood function from an initial sample and a prior information of the parameter according to the Bayes' rule. So, the main interest of the Bayesian point …
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Empirical Bayes estimators for the cross-product ratio of 2x2 contingency tables
… themselves as random variables with unknown prior distribution. Without knowledge of the exact prior distribution the Bayes estimator cannot be obtained. However, as long as independent repetitions of the experiment occur, the empirical Bayes approach can then be applied. A general strategy …
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Mitigating the Effects of Intersymbol Interference: Algorithms and Analysis
… taps as stochastic quantities drawn from a known prior distribution and uses Bayesian techniques to compute estimates of the transmitted symbols. To implement the proposed receiver, we employ a stacklike algorithm, which estimates the transmitted bits by navigating the tree generated by the …
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A pseudo-Bayesian model-based approach for noninvasive intracranial pressure estimation
… The likelihoods are combined with a imulti-modal prior distribution of the ICP to yield an a posteriori distribution whose mode is taken as the final ICP estimate. An extension to this method is proposed to harness the temporal evolution of past ICP estimates for reducing dependence on the …
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Contributions to the Analysis of Experiments Using Empirical Bayes Techniques
Specifying a prior distribution for the large number of parameters in the linear statistical model is a difficult step in the Bayesian approach to the design and analysis of experiments. Here we address this difficulty by proposing the use of functional priors and then by working out important …
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Systematic asset allocation using flexible views for South African markets
… environments, and derives a forward-looking distribution that is consistent with this view while remaining as close as possible to the prior distribution. The framework derives the forward-looking distribution by applying unequal time and state conditioned probabilities to historical …
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Using Dirichlet Process Priors For Bayesian Mixture Clustering
… of allele frequencies that follow multinomial distributions. The Dirichlet Process is applied as the prior distribution. The method estimates the number of populations together with the allele frequencies and the ancestry coefficients of each individual. Distance matrices and bootstrap support …
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Hierarchical Bayesian approaches to seismic imaging and other geophysical inverse problems
… these smoothness assumptions take the form of a prior distribution on the model parameters. Conventionally, the regularization parameters defining these assumptions are fixed independently from the data or tuned in an ad hoc manner. However, it is often the case that the smoothness properties of …
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Inference for Populations: Uncertainty Propagation via Bayesian Population Synthesis
In this dissertation, we develop a new type of prior distribution, specifically for populations themselves, which we denote the Dirichlet Spacing prior. This prior solves a specific problem that arises when attempting to create synthetic populations from a known subset: the unfortunate reality that …
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Use of prior distributions from aerial photographs in forest inventory
… stands) using aerial photo volume tables as the prior information source. Aerial photographs provided a reliable source of information even though most photographs were nearly five years old. For a given level of precision within a particular stand, Bayesian methods reduced the required field …
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Compound-Gaussian-regularized inverse problems: theory, algorithms, and neural networks
… certain assumptions, such as the image prior distribution, into an iterative estimation algorithm, often, as an example, solving a regularized least squares problem. Instead, data-driven methods learn the inverse reconstruction mapping directly by training a neural network structure on …
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Bayesian Analysis of Linear Inverse Problems with Applications in Economics and Finance
… to the inference problem is the posterior distribution of this parameter. A regular version of the posterior distribution in functional spaces is characterized. However, the infinite dimension of the considered spaces causes a problem of non continuity of the solution and then a problem of …
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A multi-state model of treatment states in an antiretroviral treatment programme cohort in Cape Town
… chain Monte Carlo were used to obtain posterior distributions of parameters. Several scenarios were used in sensitivity testing, including varying the threshold used to define potential treatment interruption periods, and either adjusting or excluding the data of those with CD4 counts that drop …
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SELF-ENHANCED VOCABULARY LEARNING LATENT DIRECHLET ALLOCATION
… rolling window training of parameters of prior distribution to handle the sparsity problem. Especially, a larger base of information is utilized to finetune the corpus-level parameters. Then the LDA module will identify the coherence of topics-documents and word-topics. Empirical results …
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A two-stage experimental design procedure under dispersion effects
… are valid. These assumptions are formulated as a prior distribution and effectively stabilize the variance estimation in the first stage through a Bayes estimator.
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Compressive sensing based imaging via belief propagation
… that all the unknown variables have the same prior distribution as we do not have any knowledge of the side information available during the initiation of the decoding process. Thus, we prove that this algorithm is effective even in the absence of side information.
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Some parametric empirical Bayes techniques
… aspect considered is that of estimating the prior distribution and then the estimation of posterior distribution and confidence intervals. In the first aspect considered we assume that there exists an unobservable parameter space 𝔏={λ} on which is defined a prior distribution G(λ). For any …
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Geography: Its Place in Higher Education Enrollment
… possess. Hierarchical Bayesian models use the prior distribution, and likelihood of an events occurrence to create the posterior distribution or Bayesian inference. The intelligence created by combining traditional recruiting techniques with GISystems and Hierarchical Bayesian modeling will …
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