Markov Chain Monte Carlo on optimal adaptive sampling selections

研究成果: Article同行評審

8 引文 斯高帕斯(Scopus)


Under a Bayesian population model with a given prior distribution, the optimal sampling strategy with a fixed sample size n is an n-phase adaptive one. That is, the selection of the next sampling units should sequentially depend on the information obtained from the previously selected units, including the observed values of interest. Such an optimal strategy is in general not executable in practice due to its intensive computation. In many survey sampling situations, an important problem is that one would like to select a set of units in addition to a certain number of sampling units which have been observed. If the optimal strategy is an adaptive one, the selection of the additional units should take both the labels and the observed values of the already selected units into account. Hence, a simpler optimal two-phase adaptive sampling strategy under a Bayesian population model is proposed in this article for practical interest. A Markov chain Monte Carlo method is used to approximate the posterior joint distribution of the unobserved population units after the first phase sampling, for the optimal selection of the second phase sample. This approximation method is found to be successful to select the optimal second-phase sample. Finally, this optimal strategy is applied to a set of data from a study of geothermal CO2 emissions in Yellowstone National Park as a practical illustrative example.

頁(從 - 到)129-151
期刊Environmental and Ecological Statistics
出版狀態Published - 2003 3月

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 一般環境科學
  • 統計、概率和不確定性


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