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this the doc related to the Bayesian Inference for Dynamic Models with Dirichlet Process Mixtures-this is the doc related to the Bayesian Inference for Dynamic Models with Dirichlet Process Mixtures
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压缩传感是一个从2006年左右开始兴起的研究领域，它关注于如何采样信号，也就是信号的采样方式或者压缩方式。通过设计一种特殊的采样方案，可以使得采样频率降低为信号的“信息率”，而不是传统的奈奎斯特采样率，于是，实际的采样率可以大大低于奈奎斯特频率，却只损失很少的信息量，依然保持了充足的信息量足以恢复出采样前的原始信号。这个研究思想挑战了奈奎斯特频率的理论极限，会对整个信号处理领域产生极其深远的影响，同时，信号处理的许多应用领域也会随之发生根本性的发展和变化。
-Compressive sens
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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
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胎儿心电信号提取，用贝叶斯方法，和神经网络接近。很详细！-FECG Extraction Using Bayesian Inference and Neural Networks Approximation
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Dynamic Bayesian Networks Representation, Inference And Learning - Kevin Patrick Murphy
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In this paper, we propose a Bayesian inference-based recommendation system for online social networks. In our system,users share their content ratings with friends. The rating similarity between a pair of friends is measured by a set of conditional p
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Speech processing applications such as speech enhancement and speaker identification rely on the estimation of relevant parameters from the speech signal. These
parameters must often be estimated from noisy observations since speech signals are
r
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此文献资料很好的概括了贝叶斯用于解决DOA估计的问题-Off-grid Direction of Arrival Estimation Using Sparse Bayesian Inference
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Artificial Intelligence lecture introduction 5/5
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