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 20 for “"Posterior Predictive"”.
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Non-Parametric Priors for Functional Data and Partition Labelling Models
… that there are significant differences in their posterior predictive perfor-</p><p>mance. Further investigation of the generalized functional Dirichlet process reveals</p><p>that a more fundamental difference exists. Whereas marginal labelling models nec-</p><p>essarily assign labels only at …
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Empirical models for cyclic voltammograms
… a Bayesian framework, we are able to<br/>obtain posterior predictive distributions for characteristics of the voltammogram of<br/>interest to chemists.<br/><br/>Markov Chain Monte Carlo sampling methods are used to explore the posterior<br/>distribution of the model parameters and to estimate the …
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Statistical methods in a high school transcript survey
… estimates and measures the relative inflation of posterior mean squared error in the posterior</p> <p>predictions is developed to evaluate the performance of hierarchical</p> <p>models. Both numerical and graphical summaries of the posterior predictive discrepancy measures are available. The …
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Model sensitivity to prior selection in replication studies
… framework. By using sensitivity analyses, posterior predictive checking, and information criteria, researchers can start with a more reasonable prior setting that eventually leads to more valid confirmation or non-confirmation of previous research.
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Goal-oriented inference : theoretical foundations and application to carbon capture and storage
… the target of our goal-oriented inference is the posterior predictive probability density function representing the relative likelihood of predictions given the observed experimental data. In many nonlinear settings, particularly those involving nonlinear partial differential equations, …
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Mixed-Variable Bayesian Optimization using Prior-Data Fitted Networks
… and in-context learning to approximate posterior predictive distributions (PPDs) in a single forward pass. By training on large amounts of synthetically generated data from sample-able function priors, PFNs can learn to rapidly predict PPDs across a wide range of function classes. In …
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Fast EM Based Posterior Approximation for IRT Item Parameters
… that has recently become popular for this is posterior predictive model checking (PPMC). This is commonly implemented using the joint posterior distribution of the item parameters as estimated through Markov chain Monte Carlo (MCMC). MCMC estimation is typically very computationally intensive …
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Bayesian Active Structure Learning for Gaussian Process Probabilistic Programs
… we also formulate a second objective function, Predictive Information Gain (IG-P), that reduces uncertainty over the posterior predictive distribution. We empirically validate that active learning with our novel IG-K objective is able to more accurately infer the structure of synthetic datasets …
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Graphical and Bayesian Analysis of Unbalanced Patient Management Data
… variable was INR. Machines were compared using posterior predictive distributions of the absolute distance outside a patient's therapeutic range. For the beta-binomial models, the machine-identical model had the lower DIC, meaning that POC device was not a strong predictor of success in keeping …
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Bayesian adaptive designs for non-inferiority and dose selection trials.
… in a Bayesian adaptive design that uses joint posterior predictive probabilities of safety and efficacy to determine adaptive allocation probabilities. Results from a retrospective study and a simulation are used to illustrate use of the method. We also present a Bayesian adaptive approach to …
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Predictive Alternatives in Bayesian Model Selection
… The first criterion is based purely on posterior predictive densities and Kullback-Leibler divergences and decomposes into terms that describe the fit and complexity of the model. In this manner, it behaves similar to popular criteria, such as the AIC or the DIC. I then present the …
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Approximate Inference in Bayesian Neural Networks and Translation Equivariant Neural Processes
… methods in approximating the Bayesian predictive distribution. We show that for single-hidden layer networks with ReLU activation functions, there are fundamental limitations concerning the representation of in-between uncertainty: increased uncertainty in between well separated regions …
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Genetic stock structure and estimation of abundance of swordfish (Xiphias gladius) in South Africa
… of South Africa's coastline was utilised. A posterior predictive map of admixture proportions produced a potential admixture zone between 14°E and 27°E. There is evidence of gene flow and migration in this area in both directions, though the evidence for weak differentiation suggests that the …
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Spatial Infectious Disease Transmission Models: Variable Screening Methods and Logistic Formulation.
… and its performance is evaluated using a posterior predictive approach (Gardner, 2010). Moreover, we apply variable selection methods to the newly developed CL-ILMs to enhance model performance, improve interpretability, and minimize the risk of overfitting, ultimately leading to more …
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Skew Normal Bayesian Asset Allocation
… with market historical data to derive a posterior distribution of portfolio returns and optimal asset allocations under the assumption of normal returns. Many studies show that normality assumption is not empirically supported and turns out to be inappropriate in many cases because of the …
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Cognitive diagnosis modeling and applications to assessing learning
… models using Deviance Information Criterion and posterior predictive probabilities.
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Optimizing wastewater treatment sampling strategies through Markov Chain Monte Carlo Bayesian inference in Activated Sludge Model No. 3
… to deliver robust parameter estimates and predictive uncertainty bounds across diverse monitoring designs. The methodology combines normalised sensitivity analysis to identify negligible parameters, profile likelihood analysis to distinguish estimable from non-estimable parameters, and …
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Contributions to quality improvement methodologies and computer experiments
… on average quadratic loss computed as if the posterior mean were the true response function, which can give misleading results. We propose optimization criteria derived by taking expectation of the average quadratic loss with respect to the posterior predictive process, and methods based on …
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An Item Response Theory Approach to the Maintenance of Standards in Public Examinations in England
… An evaluation of the literature suggests that predictive statistical models, where employed in the maintenance of standards to meet definitions of cohort referencing, tend to be robust. Beyond discrimination, measures of performance standards are required to support inferences drawn from grades …
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A tripartite study on Bayesian estimation of photosynthetically active radiation, impacts of future climate, and adaptation strategies on crop production: a spatial model framework for the Eastern Kansas River Basin
… that daily PAR values generated from the posterior predictive distribution using the Gibbs sampler algorithm outperformed the PAR estimates from the traditional linear modeling approach. In addition, soybean yield estimates using PAR from the Bayesian framework as inputs, captured the …