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.
Results
Showing 1 to 13 of 13 for “"Bayesian Markov Chain Monte Carlo"”.
-
A Bayesian Markov Chain Monte Carlo approach to the generalized graded unfolding model estimation: the future of non-cognitive measurement
… by using a state-of-the-art estimation method––Bayesian Markov Chain Monte Carlo estimation. A series of studies were conducted to test the estimation accuracy of the new software. The results clearly showed that the Bayesian MCMC estimation method outperformed the traditional MML method, in …
-
Three essays on biofuel, weather and corn yield
… soil, crop and plant environment controls. Bayesian Markov Chain Monte Carlo approach is applied to estimate the parameters and the thresholds simultaneously. Including recent two drought years 2011 and 2012 to have more drought observations in the modern eras, Chapter 4 revisits previous …
-
Sampled ancestors and dating in Bayesian phylogenetics
… a disease (either directly or through a chain of transmissions) to other sampled patients. Similarly, in palaeontology, cladograms of fossil taxa traditionally have not contained sampled ancestors. Recently, it has been recognised that the probability of sampled ancestors is not …
-
Models and methods for computationally efficient analysis of large spatial and spatio-temporal data
… geostatistical data, a latent spatial Gaussian Markov random field (GMRF) with an EAR model prior is applied. The GMRF is defined on a fine grid and thus enables the posterior precision matrix to be diagonal through introducing a missing data scheme. This results in parameter estimation and …
-
Bayesian phylogenetic models for relaxed clock and trait evolution
Bayesian Markov chain Monte Carlo (MCMC) has become a common approach for phylogenetic inference. While huge amount of data provides signi cant information of evolution, phylogenetic inference of larger data sets requires more e cient MCMC methods. In the meantime, it remains challenging to …
-
A nested random effects model analysis of child survival in Malawi
… model are estimated using the Gibbs sampler, a Bayesian Markov Chain Monte Carlo (MCMC) method. We believe this to be the first time the method has been used for this type of data. The parameters are also estimated using the expectation-maximisation (EM) algorithm, a method that has previously …
-
Essays on Bayesian Macroeconometrics
… and hours. I take the model to the data using Bayesian methods. The fluctuations caused by expectations changes from the downturn risk shock account for substantial output variations at business cycle frequencies and hours fluctuations at medium run frequencies. The extracted time-varying …
-
A Comparison of Bayesian Estimation Techniques in a Multidimensional Two-Parameter Partial Credit Item Response Model
<p>Bayesian estimation methods have shown better performance than the traditional Marginal Maximum Likelihood (MML) estimation method for parameter estimation in relatively simple item response models. However, extant literature is lacking on the investigation of Bayesian parameter estimation …
-
Essays on the economics of racial segregation
… precision. To overcome this problem, I propose a Bayesian Markov Chain Monte Carlo method that allows estimation of the posterior without evaluating the likelihood. I study segregation in social networks using data from Add Health, a survey of US high schools, containing detailed information on …
-
Essays On Spatial Econometrics: Estimation Methods And Applications
… distribution. Finally, through a comprehensive Monte Carlo simulation, we compare the finite sample properties of the robust GMM estimator with other estimators proposed in the literature. </p> <p>In the second essay, the finite sample properties of heteroskedasticity robust estimators suggested …
-
Population Pharmacokinetics of Therapeutic Monoclonal Antibodies: Examples and Estimation Method Performance Differences
… with interaction (LAP-I) methods in NONMEM and a Bayesian Markov Chain Monte Carlo (MCMC) method in WinBUGS when applied to population PK modeling of therapeutic mAbs with nonlinear PK. The Bayesian MCMC method was evaluated with both vague and informative priors. Published findings of population …
-
Identifying Exoplanets and Unmasking False Positives with NGTS
… blended objects. Third, I innovated a joint Bayesian fitting framework for photometry, centroids, and radial velocity cross-correlation function profiles. This allows to disentangle which object (target or blend) is causing the signal and to characterise the system. My method has already …
-
Scientific deep learning for efficient modeling and uncertainty quantification in engineering systems
… Conducting uncertainty quantification with Monte Carlo methods are often infeasible because of the need to execute a large number of forward model evaluations to achieve converged distributions or statistics. For systems characterized by numerous input parameters, the response calculation is …