頁籤選單縮合
題 名 | Adaptively Scaling the Metropolis Algorithm Using Expected Squared Jumped Distance |
---|---|
作 者 | Pasarica, Cristian; Gelman, Andrew; | 書刊名 | Statistica Sinica |
卷 期 | 20:1 2010.01[民99.01] |
頁 次 | 頁343-364 |
分類號 | 319.5 |
關鍵詞 | Acceptance rates; Bayesian computation; Iterative simulation; Markov chain Monte Carlo; Multiple importance sampling; |
語 文 | 英文(English) |
英文摘要 | A good choice of the proposal distribution is crucial for the rapid convergence of the Metropolis algorithm. In this paper, given a family of parametric Markovian kernels, we develop an adaptive algorithm for selecting the best kernel that maximizes the expected squared jumped distance, an objective function that characterizes the Markov chain. We demonstrate the effectiveness of our method in several examples. |
本系統中英文摘要資訊取自各篇刊載內容。