| 書(shū)目名稱(chēng) | Discretization and MCMC Convergence Assessment | | 編輯 | Christian P. Robert | | 視頻video | http://file.papertrans.cn/282/281219/281219.mp4 | | 叢書(shū)名稱(chēng) | Lecture Notes in Statistics | | 圖書(shū)封面 |  | | 描述 | The exponential increase in the use of MCMC methods and the corre- sponding applications in domains of even higher complexity have caused a growing concern about the available convergence assessment methods and the realization that some of these methods were not reliable enough for all-purpose analyses. Some researchers have mainly focussed on the con- vergence to stationarity and the estimation of rates of convergence, in rela- tion with the eigenvalues of the transition kernel. This monograph adopts a different perspective by developing (supposedly) practical devices to assess the mixing behaviour of the chain under study and, more particularly, it proposes methods based on finite (state space) Markov chains which are obtained either through a discretization of the original Markov chain or through a duality principle relating a continuous state space Markov chain to another finite Markov chain, as in missing data or latent variable models. The motivation for the choice of finite state spaces is that, although the resulting control is cruder, in the sense that it can often monitor con- vergence for the discretized version alone, it is also much stricter than alternative methods, s | | 出版日期 | Book 1998 | | 關(guān)鍵詞 | Latent variable model; Markov chain; Markov model; Variance; algorithms; renewal theory; statistics | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4612-1716-9 | | isbn_softcover | 978-0-387-98591-6 | | isbn_ebook | 978-1-4612-1716-9Series ISSN 0930-0325 Series E-ISSN 2197-7186 | | issn_series | 0930-0325 | | copyright | Springer Science+Business Media New York 1998 |
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