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 24 for “"informative priors"”.
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Development of Informative Priors in Microarray Studies
… 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: a power prior including information from multiple previous experiments, an …
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Towards a Complete Transcriptional Regulatory Code: Improved Motif Discovery Using Informative Priors
… sampling-based approach, which incorporates informative positional priors into a probabilistic framework, to find significant motifs from high-throughput TF binding data. We use different data sources to build our positional priors and apply them to yeast ChIP-chip data: </p><p>* TFs can be …
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Sample size determination for Emax model, equivalence / non-inferiority test and drug combination in fixed dose trials.
… ED₅₀. In our simulation studies, sampling priors are used to generate the data, and non-informative priors are utilized to represent ignorance of key model parameters. Sample sizes for comparative studies are then discussed in Bayesian approach. In the absence of gold standard, sample sizes …
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Count regression models with a misclassified binary covariate : a Bayesian approach.
… In the first portion of the analysis, diffuse priors are utilized for the regression coefficients and the effective prior sample size technique is implemented to construct informative priors for the misclassification parameters. In the second portion of the analysis we place informative priors …
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Bayesian empirical likelihood for quantile regression
… efficiency gains are demonstrated with informative priors on common features across quantile levels.
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Population Pharmacokinetics of Therapeutic Monoclonal Antibodies: Examples and Estimation Method Performance Differences
… MCMC method was evaluated with both vague and informative priors. Published findings of population PK analyses of therapeutic mAbs were used to define the informative priors. Simulations were performed with uncertainty included simultaneously on all parameters in the population PK model in …
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Multilevel modelling of determinants of contraceptive method choice among women in South Africa
… models. The Bayesian analyses with non informative priors were strengthened by the use of the state of the art Hamiltonian Monte Carlo algorithm (HMC), as implemented in the RStan package in the R statistical software. The Bayesian nal model was selected based on Watanabe{Akaike …
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Examining Uncertainty and Misspecification of Attributes in Cognitive Diagnostic Models
… third set of simulations showed that using more informative priors did not uniformly improve recovery. An application of the approach to data from TIMSS (2007) suggested some alternative Q-matrices.
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COMPARING THREE ESTIMATION METHODS FOR THE THREE-PARAMETER LOGISTIC IRT MODEL
… from a non-convergence problem unless strong informative priors are specified for the item slope and intercept parameters. This study focused on comparing the three estimation methods for the three-parameter logistic (3PL) model in parameter estimation using Monte Carlo simulations. In …
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Bayesian modelling of mixed outcome types using random effect.
… and performs similarly when relatively non-informative priors are used. A Bayesian sample size determination method is also developed. We make three extensions to the PN model accounting for three complications common to Poisson-normal (PN) regression: continuous responses exhibiting …
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Objective Bayesian Analysis of Kullback-Liebler Divergence of two Multivariate Normal Distributions with Common Covariance Matrix and Star-shape Gaussian Graphical Model
… goal for this part is to derive objective/non-informative priors for the parameterizations and use these priors to build up constructive random posteriors of the Kullback-Liebler (KL) divergence of the two multivariate normal populations, which is proportional to the distance between the two …
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Extreme value theory: Applications to estimation of stochastic traffic capacity and statistical downscaling of precipitation extremes
… of censoring, a Bayesian framework using semi-informative priors was implemented. A simple cross validation procedure reveals the GEV model, using both censored and observed capacity data, is suitable for probabilistic prediction. To overcome the uncertainty associated with a high number of …
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A Predictive Modeling Approach for Assessing Seismic Soil Liquefaction Potential Using CPT Data
… two variable case and will require more data or informative priors to be adequately estimated. </p>
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A Modified Bayesian Power Prior Approach with Applications in Water Quality Evaluation
… analysis has been proven to be a useful class of informative priors in Bayesian inference. In this dissertation, we propose a modified approach to constructing the joint power prior distribution for the parameter of interest and the power parameter. The power parameter, in this modified approach, …
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Essays on the Bayesian inequality restricted estimation
… to prior specification even in the case of non-informative priors. Lowering the variance of the non-informative prior improves the Bayesian estimation, without significantly changing the nature of the distribution. In the cases where Bayesian prior variance is very large, MLE dominates the …
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Dealing with Sparse Rater Scoring of Constructed Responses within a Framework of a Latent Class Signal Detection Model
… to yield an identified model, 2) the use of informative priors in a Bayesian approach, and 3) the use of back readings (e.g., partially available 2nd rater observations), which are available in some large scale assessments. Simulations and analyses of real-world data are conducted to examine …
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BLINDED EVALUATIONS OF EFFECT SIZES IN CLINICAL TRIALS: COMPARISONS BETWEEN BAYESIAN AND EM ANALYSES
… for ordering effect sizes. Introducing informative priors for the latent variables, in settings where the EM algorithm has been used, typically improves the accuracy of parameter estimation in effect size ordering. We illustrate our method with a secondary analysis of a longitudinal …
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Contributions to Modeling and Analysis of Method Comparison Data
… Second, we develop a Bayesian approach that uses informative priors for error variances within a mixed-effect model framework. This approach allows taking advantage of information about error variances that may be available from previous studies, potentially leading to their improved estimation. …
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Optimizing wastewater treatment sampling strategies through Markov Chain Monte Carlo Bayesian inference in Activated Sludge Model No. 3
… inference, integrating literature-informed priors with synthetic observational data to characterise posterior distributions for 36 kinetic and stoichiometric parameters. Five monitoring regimes spanning intensive 14-day campaigns with four daily measurements to sparse year-long programmes …
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Repeated Measures ANOVA with Latent Variables: A New Approach Based on Structural Equation Modeling
… this dissertation will show how to place informative priors on means, (co)variances and regression coefficients that constitute a particular main or interaction effect in the L-RM-ANOVA model. This dissertation will also show how different choices of priors affect statistical properties of …
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