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[Network DevelopRECURSIVE BAYESIAN INFERENCE ON

Description:

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.


Platform: | Size: 1457664 | Author: eestarliu | Hits:

[Software EngineeringPattern Recognition and Machine Learning

Description: This leading textbook provides a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. This is the first machine learning textbook to include a comprehensive coverage of recent developments such as probabilistic graphical models and deterministic inference methods, and to emphasize a modern Bayesian perspective. It is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. This hard cover book has 738 pages in full colour, and there are 431 graded exercises (with solutions available below). Extensive support is provided for course instructors.
Platform: | Size: 7775130 | Author: arcanesky | Hits:

[matlabprobabilistic-graphical-models

Description: Probabilistic graphical models in matlab. -Probabilistic graphical models in matlab.
Platform: | Size: 417792 | Author: 王小剛 | Hits:

[Industry researchGaussian_Mixture_Models_and_Probabilistic_Decisio

Description: very good Gaussian Mixture Models and Probabilistic Decision-Based Neural Networks for Pattern Classification - A Comparative Study document -very good Gaussian Mixture Models and Probabilistic Decision-Based Neural Networks for Pattern Classification- A Comparative Study document
Platform: | Size: 287744 | Author: B | Hits:

[Documentsprobabalistic_PCA

Description: Probabilistic Principal Component Analysis – Latent variable models – Probabilistic PCA • Formulation of PCA model • Maximum likelihood estimation – Closed form solution – EM algorithm » EM Algorithms for regular PCA » Sensible PCA (E-M algorithm for probabilistic PCA) – Mixtures of Probabilistic Principal Component Analysers-Probabilistic Principal Component Analysis – Latent variable models – Probabilistic PCA • Formulation of PCA model • Maximum likelihood estimation – Closed form solution – EM algorithm » EM Algorithms for regular PCA » Sensible PCA (E-M algorithm for probabilistic PCA) – Mixtures of Probabilistic Principal Component Analysers
Platform: | Size: 263168 | Author: Tatyana | Hits:

[MultiLanguagecrf

Description: CRF最权威介绍资料,介绍了CRF的来龙去脉-Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
Platform: | Size: 154624 | Author: test | Hits:

[DocumentsCRF

Description: 条件随机场,应用进行分割和标定序列数据的概率模型.-conditional random fields, a framework for building probabilistic models to segment and label sequence data.
Platform: | Size: 154624 | Author: 许飞扬 | Hits:

[matlabKNN-complexity-reduced-method

Description: 基于LANDMARC的定位系统上进行的算法复杂度的减小的优化,包括了具体的优化后系统的实现,误差前后对比,改文章还提出了一种adaptive的定位算法,更利于外部变化环境下-In wireless networks, a client’s locations can be estimated using signal strength received from signal transmitters. Static fingerprint-based techniques are commonly used for location estimation, in which a radio map is built by calibrating signal-strength values in the offline phase. These values, compiled into deterministic or probabilistic models, are used for online localization. However, the radio map can be outdated when signal-strength values change over time due to environmental dynamics, and repeated data calibration is infeasible or expensive. In this paper, we present a novel algorithm, known as Location Estimation using Model Trees (LEMT), to reconstruct a radio map by using real-time signal-strength readings received at the reference points. This algorithm can take real-time signal-strength values at each time point into account and make use of the dependency between the estimated locations and reference points. We show that this technique can effectively accommodat
Platform: | Size: 1444864 | Author: xuchen | Hits:

[Embeded Linux2009Koller

Description: ller的大作,经典不用多言。 Probabilistic Graphical Models: Principles and Techniques是1200多页的大部头,扫描版本,我经过了优化处理,只有20MB,方便携带、传输,适合阅览、打印,请下载收藏吧,令你的科研如虎添翼!!! -Koller经典著作Probabilistic Graphical Models Principles and Techniques完美版
Platform: | Size: 19499008 | Author: Tonghua | Hits:

[Program docTRACKING-

Description: 多目标跟踪的一篇文章,写的还不错,英文的文章,可以作为目标跟踪的读物-Tracking multiple people under occlusion and across cam eras using probabilistic models
Platform: | Size: 681984 | Author: xutongxue | Hits:

[Windows DevelopCRFProbabilistic-Models-for-Segment

Description: Conditional Random Fields Probabilistic Models for Segmenting and Labeling Sequence Data
Platform: | Size: 154624 | Author: zhanghf | Hits:

[OtherBook-Draft---An-introduction-to-probabilistic-gra

Description: An introduction to probabilistic graphical models - M Jordan
Platform: | Size: 18248704 | Author: 卢彦飞 | Hits:

[Graph RecognizePattern_Recognition_and_Machine_Learning__Informa

Description: Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had significant impact on both algorithms and applications.
Platform: | Size: 4551680 | Author: sas | Hits:

[Software EngineeringProbabilistic-Graphical-Models

Description: 一个中科院的老师的关于概率图模型的讲座。介绍的内容比较新。-A Chinese Academy of Sciences of the teacher s lectures on the probabilistic graphical model. Described is relatively new.
Platform: | Size: 952320 | Author: 梁运棠 | Hits:

[matlabmmse_mrf_demo-1.1

Description: 图像去噪-A Generative Perspective on MRFs in Low-Level Vision-A Generative Perspective on MRFs in Low-Level Vision Markov random fields (MRFs) are popular and generic probabilistic models of prior knowledge in low-level vision. Yet their generative properties are rarely examined, while application-specific models and non-probabilistic learning are gaining increased attention. In this paper we revisit the generative aspects of MRFs, and analyze the quality of common image priors in a fully application-neutral setting. Enabled by a general class of MRFs with flexible potentials and an efficient Gibbs sampler, we find that common models do not capture the statistics of natural images well. We show how to remedy this by exploiting the efficient sampler for learning better generative MRFs based on flexible potentials. We perform image restoration with these models by computing the Bayesian minimum mean squared error estimate (MMSE) using sampling. This addresses a number of shortcomings that have limited generative MRFs so far, and le
Platform: | Size: 1216512 | Author: 孙文义 | Hits:

[Windows DevelopCRFProbabilistic-Models-for-Segment

Description: Conditional Random Fields Probabilistic Models for Segmenting and Labeling Sequence Data -Conditional Random Fields Probabilistic Models for Segmenting and Labeling Sequence Data
Platform: | Size: 154624 | Author: electedivi | Hits:

[OtherMachine-Learnin

Description: 机器学习(Machine Learning, ML)是一门多领域交叉学科,涉及概率论、统计学、逼近论、凸分析、算法复杂度理论等多门学科。专门研究计算机怎样模拟或实现人类的学习行为,以获取新的知识或技能,重新组织已有的知识结构使之不断改善自身的性能-Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had significant impact on both algorithms and applications.
Platform: | Size: 7962624 | Author: 王以良 | Hits:

[Program doc2001-attias-eurospeech

Description: Speech Denoising and Robust Speech Recognition Using Probabilistic Models
Platform: | Size: 57344 | Author: chawki | Hits:

[OtherProbabilistic-Graphical-Models

Description: Probabilistic graphical models are now widely accepted as a powerful and mature technology for reasoning under uncertainty, and there are many efficient algorithms for both inference and learning available in open-source and commercial software.
Platform: | Size: 5819392 | Author: tianwhuyh | Hits:

[BooksProbabilistic Graphical Models using Python

Description: Probabilistic Graphical Models using Python(Probabilistic Graphical Models using Python, GOOD!)
Platform: | Size: 230400 | Author: sz1liuyong | Hits:
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