반복 표본추출방법을 이용한 주변밀도함수에 관한 연구 = Iterative, Sampling-based Methods to Calculating Marginal Densities

다국어 초록 (Multilingual Abstract)

Computer-intensive algorithms such as iterative, sampling-based method, have become increasingly popular satatistical tools, both in applied and theoretical work.
The properties of such algorithms, however, may sometimes not be obvious.
Here we give a simple explanation of how and why the iterative, sampling-based method works. We analytically establish its properties in a simple case and provide insight for more complicated cases. There are also a number of examples.
For the Bayesian, the Gibbs sampler is mainly used to generate posterior distributions, whereas for the classical statistician a major use is for calculation of the likelihood function and characteristics of likelihood estimators.