While most statistical applications involve real numbers, some demand
complex numbers. Statistical shape analysis is one such area. The complex
Bingham distribution is utilized in the shape analysis of landmark data in two dimensions.
Previous analysis of data arising from this distribution involved
classical statistical techniques. In this report, a full Bayesian inference was
carried out on the posterior distribution of the parameter matrix when data
arise from a complex Bingham distribution. We utilized a Markov chain Monte
Carlo algorithm to sample the posterior distribution of the parameters. A
Metropolis-Hastings algorithm sampled the posterior conditional distribution
of the eigenvalues while a successive conditional Monte Carlo sampler was used
to sample the eigenvectors. The method was successfully verifi ed on simulated
data, using both
at and informative priors. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/ETD-UT-2010-12-2587 |
| Date | 21 February 2011 |
| Creators | Leu, Richard Hsueh-Yee |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
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