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 54 for “"Prior Distributions"”.
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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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Context-Driven Prior Distributions in Genome–Wide Association Studies, Medical Device Adaptive Clinical Trials, and Genetic Fine-Mapping
Present research has gravitated towards making inferences from high-dimensional data, the scenario when we have considerably more variables than the number of observations we have to estimate their effects on a given outcome, and standard statistical methodology cannot be used here. Models assuming …
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Data-Driven sequential decision making with learning under ambiguity
… model that incorporates a decision maker’s prior probabilistic information to model uncertain transition probabilities in a Bayesian framework. We first construct a set of prior distributions for unknown transition probabilities. With the evidence of historical data, the prior distributions …
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Bayesian and Positive Matrix Factorization approaches to pollution source apportionment
… them in the context of PSA. The use of a priori information in PMF is examined, in the form of target factor profiles and pulling profile elements to zero. A Bayesian model using lognormal prior distributions for source profiles and source contributions is fit and examined.
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Default Bayesian model determination for generalised liner mixed models
… must address the two key issues of default prior specification and computation.<br/><br/>Default prior distributions for the model parameters, that are based on a unit information concept, are proposed.<br/><br/>A two-phase computational strategy, that uses a reversible jump algorithm and …
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The effects of three different priors for variance parameters in the normal-mean hierarchical model
Many prior distributions are suggested for variance parameters in the hierarchical model. The “Non-informative” interval of the conjugate inverse-gamma prior might cause problems. I consider three priors – conjugate inverse-gamma, log-normal and truncated normal for the variance parameters and do …
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Comparison of Bayes' and minimum variance unbiased estimators of reliability in the extreme value life testing model
… for the uniform, exponential, and inverted gamma prior distributions are obtained, and these results are extended to a whole class of exponential failure models. Each of the Bayes' estimators is compared with the unbiased minimum variance estimator in a Monte Carlo simulation where it is shown …
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Complete classes of two-stage estimation procedures for certain finite sample problems
… for estimating the mean of exponential family distributions are given by Cohen and Sackrowitz (1984). In their study, they develop double sample Bayes estimation procedures for the mean of exponential family distributions with respect to conjugate prior distributions. The procedures consist of …
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Topics in Bayesian sample size determination and Bayesian model selection.
… added. Simulations demonstrate that choices of prior distributions have a great impact on the resultant sample size. The last topic is about Bayesian variable selection under the multiple regression model. Two competing Bayesian methods are Bayesian model averaging and reversible jump MCMC. It …
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Development of Informative Priors in Microarray Studies
… data because of their ability to integrate prior information; however, most current Bayesian analyses use empirical or flat priors. We present a Perl script to build an informative prior by mining online databases for similar microarray experiments. Four prior distributions are investigated: …
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Probabilistic search: a Bayesian approach in a continuous workspace
… over the workspace. Given a likelihood and prior belief belonging to the exponential family class, while using this class's self-conjugacy property, an exact, finite representation of the object posterior is explicitly derived. Though complexity issues may render this exact representation …
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Dynamic Matching of Users and Creators on Social Media Platforms
… users and creators are randomly generated from prior distributions, but explicitly maximizing long-term engagement is NP-hard. Finally, we present new practical algorithms with provable guarantees and good empirical performance.
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Applying an Intrinsic Conditional Autoregressive Reference Prior for Areal Data
… (ICAR) models are commonly assigned as priors for spatial random effects in hierarchical models for areal data corresponding to spatial partitions of a region. However, selection of prior distributions for these spatial parameters presents a challenge to researchers. We present and …
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First and Second Order Efficiency of Sequential Designs in a Nonlinear Situation with Applications
… on the parameters, which are unknown a priori. In such circumstances, choosing the next design points adaptively by making good use of information being collected so far, namely, designing the experiment sequentially, is appropriate. In this study, the problem of estimating a nonlinear …
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Setting location priors using beamforming improves model comparison in MEG-DCM
… inform- ing the MEG DCM source location with prior distributions defined using a MEG source localization algorithm improves model selection accuracy. DCM inversion of a group of candidate models shows an enhanced ability to identify a ground-truth network structure when source-localized prior …
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Minimally Corrective, Approximately Recovering Priors to Correct Expert Judgement in Bayesian Parameter Estimation
… to address inverse problems. However, since prior distributions are chosen based on expert judgement, the method can inherently introduce bias into the understanding of the parameters. This can be especially relevant in the case of distributed parameters where it is difficult to check for …
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Bayesian Model Uncertainty and Prior Choice with Applications to Genetic Association Studies
… literature has focused on defining these prior distributions in an objective manner, providing conditions under which Bayes factors lead to the correct model selection, particularly in the situation where the number of variables, <italic>p</italic>, increases with the sample size, …
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Modelling of Dynamic Computer Experiments with Both Qualitative and Quantitative Variables
… We also explored the different choices of the prior distributions for the unknown parameters in these two models. The prediction accuracy of the two proposed modelling approaches is tested by a limited simulation study.
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The application of Bayesian adaptive design and Markov model in clinical trials
… in the model is derived using appropriate prior distributions. Markov Chain Monte Carlo (MCMC) method is used to do the simulation. Model parameters with meaningful prior distributions and the posterior quantities are obtained to evaluate the trial results and they help to determine the …
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Bayesian methods to estimate the accuracy of diagnostic tests in meta-analysis models.
… We begin by developing a hierarchical Bayesian prior structure to estimate prevalences and misclassi cation rates for a single diagnostic test. We provide the results from a simulation study which shows that this model has desirable operating characteristics. We then adapt the model to analyze a …
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