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A Topics Analysis Model For Health Insurance Claims, Jared Anthony Webb
A Topics Analysis Model For Health Insurance Claims, Jared Anthony Webb
Theses and Dissertations
Mathematical probability has a rich theory and powerful applications. Of particular note is the Markov chain Monte Carlo (MCMC) method for sampling from high dimensional distributions that may not admit a naive analysis. We develop the theory of the MCMC method from first principles and prove its relevance. We also define a Bayesian hierarchical model for generating data. By understanding how data are generated we may infer hidden structure about these models. We use a specific MCMC method called a Gibbs' sampler to discover topic distributions in a hierarchical Bayesian model called Topics Over Time. We propose an innovative use …
Markov Chain Monte Carlo Simulation Of The Wright-Fisher Diffusion, Markus Joseph Wahl
Markov Chain Monte Carlo Simulation Of The Wright-Fisher Diffusion, Markus Joseph Wahl
Theses and Dissertations
In population genetics, the proportions of alleles at any given time are of interest. From generation to generation, these proportions vary and over a long time horizon the likelihoods for the proportions are given by a stationary distribution corresponding to the dynamics of the population. We investigate a diffusion approximation for the Wright-Fisher model and develop a Markov chain Monte Carlo simulation to approximate the evolution of the proportions of alleles in the population. Our aim is to estimate the stationary distribution, especially for parameters of the model for which no analytical formulas are known. We discretize the space of …