The need for accurate monitoring and AnalySis of sequential data arises in many scientic, industrial and nancial problems. Although the Kalman lter is effective in the linear-Gaussian case, new methods of dealing with sequential data are required with non-standard models. Recently, there has been renewed interest in simulation-based techniques. The basic idea behind these techniques is that the current state of knowledge is encapsulated in a representative sample from the appropriate posterior distribution. As time goes on, the sample evolves and adapts recursively in accordance with newly acquired data. We give a critical review of recent developments, by reference to oil well monitoring, ion channel monitoring and tracking problems, and propose some alternative algorithms that avoid the weaknesses of the current methods.
標(biāo)簽： monitoring sequential industria accurate
上傳時(shí)間： 2013-12-17
上傳用戶：familiarsmile
漢語(yǔ)詞法分析系統(tǒng)ICTCLAS(Institute of Computing Technology, Chinese Lexical AnalySis System)
標(biāo)簽： Technology Computing Institute AnalySis
上傳時(shí)間： 2016-01-04
上傳用戶：mhp0114
Degraded Image AnalySis:An Invariant Approach一篇經(jīng)典的關(guān)于不變矩的文章。
標(biāo)簽： Invariant Degraded AnalySis Approach
上傳時(shí)間： 2016-01-05
上傳用戶：ddddddos
AnalySis and Hardware Design of Intermediate View Generation Using Adaptive Disparity Estimation Based on Spatial and Temporal Correlation
標(biāo)簽： Intermediate Generation Estimation Disparity
上傳時(shí)間： 2016-01-10
上傳用戶：siguazgb
ICA介紹課件。There has been a wide discussion about the application of Independence Component AnalySis (ICA) in Signal Processing, Neural Computation and Finance, first introduced as a novel tool to separate blind sources in a mixed signal. The Basic idea of ICA is to reconstruct from observation sequences the hypothesized independent original sequences
標(biāo)簽： Independence application discussion Component
上傳時(shí)間： 2016-01-12
上傳用戶：AbuGe
AnalySis for DRM and WMA. about the cost of decoding time and memroy.
標(biāo)簽： and AnalySis decoding memroy
上傳時(shí)間： 2016-01-27
上傳用戶：xieguodong1234
Simulation Modeling,Discrete Event Simulation,Statistical AnalySis of Simulation Models
標(biāo)簽： Simulation Statistical Modeling Discrete
上傳時(shí)間： 2013-12-16
上傳用戶：
Simulation Modeling,Statistical AnalySis of Simulation Models,Discrete Event Simulation
標(biāo)簽： Simulation Statistical Modeling AnalySis
上傳時(shí)間： 2014-01-13
上傳用戶：kernaling
Simulation Modeling,Statistical AnalySis of Simulation Models,Discrete Event Simulation
標(biāo)簽： Simulation Statistical Modeling AnalySis
上傳時(shí)間： 2014-06-22
上傳用戶：guanliya
PCA and PLS aims:to get some insight into the bilinear factor models Principal Component AnalySis (PCA) and Partial Least Squares (PLS) regression, focusing on the mathematics and numerical aspects rather than how s and why s of data AnalySis practice. For the latter part it is assumed (but not absolutely necessary) that the reader is already familiar with these methods. It also assumes you have had some preliminary experience with linear/matrix algebra.
標(biāo)簽： Component Principal AnalySis bilinear
上傳時(shí)間： 2016-02-07
上傳用戶：zuozuo1215
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