MCMC can detect nonidentifiable models.
MCMC can detect nonidentifiable models.
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MCMC 可以检测不可识别的模型。
DOI：
10.1016/j.bpj.2012.10.024
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发表时间：
2012
影响因子：
3.4
通讯作者：
Crampin,EdmundJ
中科院分区：
文献类型：
--
作者：
Siekmann,Ivo;Sneyd,James;Crampin,EdmundJ
连续时间马尔可夫模型一直被认为是最好的代表性的随机动力学的离子通道超过三十年。对于大多数单通道数据集，需要几个打开和关闭状态来准确地表示动态。然而，每个数据点仅显示通道是打开还是关闭，而不是处于哪个状态。因此，一些模型结构本质上是过度参数化的，因此原则上不适合表示任何数据这些模型被称为“不可识别”。在撰写本文时，人们似乎对哪些连续时间马尔可夫模型是可识别的，哪些是不可识别的知之甚少，因此无意识地使用不可识别的模型是一个相当大的问题。为了解决这个问题，最近发表的马尔可夫链蒙特卡罗方法的一个改进的变种。使用测试数据以及实验数据对该算法进行了测试。我们证明，相对于广泛使用的最大似然估计，它给出了明确的警告信号时，一个不可识别的模型用于拟合。此外，对于从不可识别模型生成的测试数据，马尔可夫链蒙特卡罗结果从数据中恢复比最大似然估计更多的信息。
Continuous-time Markov models have been considered the best representation for the stochastic dynamics of ion channels for more than thirty years. For most single-channel data sets, several open and closed states are required for accurately representing the dynamics. However, each data point only shows if the channel is open or closed but not in which state it is. Consequently, some model structures are inherently overparameterized and therefore, in principle, unsuitable for representing any data—those models are called "nonidentifiable". As of this writing, it seems to be poorly understood which continuous-time Markov models are identifiable and which are not, therefore the unconscious use of a nonidentifiable model is a considerable concern. To address this problem, an improved variant of a recently published Markov-chain Monte Carlo method is presented. The algorithm is tested using test data as well as experimental data. We demonstrate that, opposed to a widely used maximum-likelihood estimator, it gives clear warning signs when a nonidentifiable model is used for fitting. Furthermore, for test data that was generated from a nonidentifiable model, the Markov-chain Monte Carlo results recover much more information from the data than maximum-likelihood estimation.
DOI：
10.1098/rspa.1999.0432
发表时间：
1999-08-08
影响因子：
3.5
作者：
Ball, FG;Cai, Y;O'Hagan, A
通讯作者：
O'Hagan, A
使用马尔可夫链蒙特卡罗在离子通道马尔可夫模型中进行贝叶斯选择
DOI：
--
发表时间：
1999
期刊：
Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences
影响因子：
--
作者：
M. E. A. Hodgson;P. Green
通讯作者：
P. Green
DOI：
10.1098/rspb.1989.0024
发表时间：
1989-04-22
期刊：
PROCEEDINGS OF THE ROYAL SOCIETY SERIES B-BIOLOGICAL SCIENCES
影响因子：
--
作者：
KIENKER, P
通讯作者：
KIENKER, P