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Improving The Computational Efficiency In Bayesian Fitting Of Cormack-Jolly-Seber Models With Individual, Continuous, Time-Varying Covariates, Woodrow Burchett
Improving The Computational Efficiency In Bayesian Fitting Of Cormack-Jolly-Seber Models With Individual, Continuous, Time-Varying Covariates, Woodrow Burchett
Theses and Dissertations--Statistics
The extension of the CJS model to include individual, continuous, time-varying covariates relies on the estimation of covariate values on occasions on which individuals were not captured. Fitting this model in a Bayesian framework typically involves the implementation of a Markov chain Monte Carlo (MCMC) algorithm, such as a Gibbs sampler, to sample from the posterior distribution. For large data sets with many missing covariate values that must be estimated, this creates a computational issue, as each iteration of the MCMC algorithm requires sampling from the full conditional distributions of each missing covariate value. This dissertation examines two solutions to …