| Summary: | Various Markov chain Monte Carlo algorithms are available for sampling
from a posterior distribution. The random walk Metropolis algorithm is a
simple scheme which is frequently used in Bayesian statistical problem. The
guided walk algorithm attempts to suppress the random walk behavior in the
random walk Metropolis algorithm. Other algorithms, such as the Langevin
algorithm and the hybrid algorithm use more information about the posterior
distribution than the random walk Metropolis algorithm and the guided walk
algorithm. In this thesis, The performance of each of those four algorithms
has been examined, based on simulation studies using multivariate normal
target distributions. Then we compare the algorithms in terms of efficiency
and convergence time. Moreover, these four algorithms are compared using a
posterior distribution for parameters given observed data in an application. === Science, Faculty of === Statistics, Department of === Graduate
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