DL : 0
This thesis is concerned with recursive Bayesian estimation of non-linear dynamical
systems, which can be modeled as discretely observed stochastic differential
equations. The recursive real-time estimation algorithms for these continuous-
discrete filtering problems are traditionally called optimal filters and the algorithms
for recursively computing the estimates based on batches of observations
are called optimal smoothers. In this thesis, new practical algorithms for approximate
and asymptotically optimal continuous-discrete filtering and smoothing are
presented.
The mathematical approach of this thesis is probabilistic and the estimation
algorithms are formulated in terms of Bayesian inference. This means that the
unknown parameters, the unknown functions and the physical noise processes are
treated as random processes in the same joint probability space. The Bayesian approach
provides a consistent way of computing the optimal filtering and smoothing
estimates, which are optimal given the model assumptions and a consistent
way of analyzing their uncertainties.
The formal equations of the optimal Bayesian continuous-discrete filtering
and smoothing solutions are well known, but the exact analytical solutions are
available only for linear Gaussian models and for a few other restricted special
cases. The main contributions of this thesis are to show how the recently developed
discrete-time unscented Kalman filter, particle filter, and the corresponding
smoothers can be applied in the continuous-discrete setting. The equations for the
continuous-time unscented Kalman-Bucy filter are also derived.
The estimation performance of the new filters and smoothers is tested using
simulated data. Continuous-discrete filtering based solutions are also presented to
the problems of tracking an unknown number of targets, estimating the spread of
an infectious disease and to prediction of an unknown time series.
Update : 2009-02-01
Size : 1457664
Publisher : eestarliu
DL : 0
Bayesian-machine-learn-data for robot
Update : 2012-07-05
Size : 303855
Publisher : yhaald
DL : 0
贝叶斯bayes算法分类器诊断程序-Bayesian classifier diagnostic procedures
Update : 2024-04-30
Size : 57344
Publisher : 陈响
DL : 0
利用贝叶斯算法实现的分类器-algorithm using Bayesian classifier
Update : 2024-04-30
Size : 27648
Publisher : 王勇
DL : 0
模式识别_贝叶斯分类器-pattern recognition _ Bayesian classifier
Update : 2024-04-30
Size : 72704
Publisher : 耀西
DL : 0
贝叶思网络分类规则例程-Bayesian Network Classification Rule tutorial
Update : 2024-04-30
Size : 8192
Publisher : 胡斌
DL : 0
朴素贝叶斯分类器(Navie Bayesian Classifier)识别鼠标输入的字母A~J-Naive Bayesian classifier (Navie Bayesian Classifier) Identification of the mouse input letters A-J
Update : 2024-04-30
Size : 84992
Publisher : 李勃东
DL : 0
一个用来做模式识别的贝叶斯分类器-used as a pattern recognition Bayesian classifier
Update : 2024-04-30
Size : 72704
Publisher : 李丰华
DL : 0
贝叶斯算法(matlab编写) 安装,添加目录 /home/ai2/murphyk/matlab/FullBNT-Bayesian algorithm (Matlab preparation) installed, add directory/home/ai2/murphyk/matlab/FullBNT
Update : 2024-04-30
Size : 1720320
Publisher : hu min
DL : 0
人工智能实验§贝叶斯网络的因果推理关系§ 熟悉掌握Bayes定理,学习贝叶斯网络的因果推理-artificial intelligence experiments Bayesian network causal reasoning relations mastery Bayes Theorem, learning Bayesian network causal reasoning
Update : 2024-04-30
Size : 104448
Publisher : 楚随风
DL : 0
基于贝叶斯网络模型的用户兴趣联合推送,分析了用户兴趣模型的优势-Bayesian network model based on user interest joint push, analysis of the advantages of user interest model
Update : 2024-04-30
Size : 424960
Publisher : hailong
DL : 0
贝叶斯分类亲,功能强大,适合于人工智能研究与应用-Bayesian classification pro, powerful, welcome to download the Blessings
Update : 2024-04-30
Size : 4096
Publisher : ji
DL : 0
In this paper, we propose a Bayesian methodology for
receiver function analysis, a key tool in determining the deep structure
of the Earth’s crust.We exploit the assumption of sparsity for
receiver functions to develop a Bayesian deconvolution method as
an alternative to the widely used iterative deconvolution.We model
samples of a sparse signal as i.i.d. Student-t random variables.
Gibbs sampling and variational Bayes techniques are investigated
for our specific posterior inference problem. We used those techniques
within the expectation-maximization (EM) algorithm to
estimate our unknown model parameters. The superiority of the
Bayesian deconvolution is demonstrated by the experiments on
both simulated and real earthquake data.
Update : 2024-04-30
Size : 3350528
Publisher : 张洋
DL : 0
基于贝叶斯的抠像算法,通过一个最大似然的标准去估计透明度-A Bayesian Approach to Digital Matting,uses a maximum-likelihood criterion to estimate
the optimal opacity
Update : 2024-04-30
Size : 5713920
Publisher : 士大夫
DL : 0
Bayesian Analysis with Python
Update : 2024-04-30
Size : 3772416
Publisher : 三石仙森
DL : 0
贝叶斯HSMM 主要是改进HMM存在状态保持时间的局限性,引入了持续时间的函数显式表达,用贝叶斯概率估计((Bayesian and HSMM was mainly to improve HMM state of existence to maintain the limitations of time, the introduction of an explicit function of the duration of expression of))
Update : 2024-04-30
Size : 15360
Publisher : summer211
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