Based on a rank-1 update, we propose Sparse Bayesian Learning Algorithm (SBLA), which has low complexity and high sparseness, thus being very suitable for large-scale problems.
基于秩-1更新，提出了稀疏贝叶斯学习算法(SBLA)。 该算法具有较低的计算复杂度和较高的稀疏性，从而适合于求解大规模问题。
参考来源 - 三种有效的核机器Chapter four is "Static and Dynamic Bayesian Learning".
第四章是探讨静态信息和动态信息的贝叶斯学习。
参考来源 - 资产误定价问题的理论研究和实证分析·2,447,543篇论文数据，部分数据来源于NoteExpress
The new algorithm is based in the property of tendency and normal distribution of consistent Bayesian learning.
新算法是以相容的贝叶斯学习的渐进正态性为理论基础。
Bayesian learning Theory represents uncertainty with probability and learning and inference are realized by probabilistic rules.
贝叶斯学习理论使用概率去表示所有形式的不确定性，通过概率规则来实现学习和推理过程。
We integrate the Sparse Bayesian Learning algorithm into the SMC blind receiver to improve the performance under sparse channels.
我们将稀疏贝叶斯学习与序列蒙特卡罗盲均衡算法结合，提高了原算法的性能。
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