Biased Online Parameter Inference for State-Space Models
Biased Online Parameter Inference for State-Space Models
复制标题
状态空间模型的有偏在线参数推断
DOI：
10.1007/s11009-016-9511-x
复制
发表时间：
2015
影响因子：
0.9
通讯作者：
Yan Zhou
中科院分区：
文献类型：
--
作者：
P. Moral;A. Jasra;Yan Zhou
我们考虑贝叶斯在线静态参数估计的状态空间模型。这是一个非常重要的问题，但是在计算上非常具有挑战性，因为精确的现有技术方法通常具有随着时间参数而增长的计算成本;也许最成功的算法是SM C2的算法（Chopin等人，J R Stat Soc B 75：397-426 2013）。我们提出了一个版本的SM C2算法的计算成本，不增长的时间参数。此外，在假设下，该算法被证明提供一致的估计的期望w.r.t.后面。然而，实现这种一致性的成本在参数空间的维度上可以是指数的;如果避免这种指数成本，则通常算法是有偏差的。从理论的角度研究的偏见，在假设下，我们发现，偏差不积累的时间参数的增长。该算法在几个贝叶斯统计模型上实现。
We consider Bayesian online static parameter estimation for state-space models. This is a very important problem, but is very computationally challenging as the state-of-the art methods that are exact, often have a computational cost that grows with the time parameter; perhaps the most successful algorithm is that of SM C2 (Chopin et al., J R Stat Soc B 75: 397–426 2013). We present a version of the SM C2 algorithm which has computational cost that does not grow with the time parameter. In addition, under assumptions, the algorithm is shown to provide consistent estimates of expectations w.r.t. the posterior. However, the cost to achieve this consistency can be exponential in the dimension of the parameter space; if this exponential cost is avoided, typically the algorithm is biased. The bias is investigated from a theoretical perspective and, under assumptions, we find that the bias does not accumulate as the time parameter grows. The algorithm is implemented on several Bayesian statistical models.
影响因子：
1.8
作者：
Beskos, Alexandros;Jasra, Ajay;Thiery, Alexandre
通讯作者：
Thiery, Alexandre