自回归模型
蒙特卡罗方法
计算机科学
选型
选择(遗传算法)
最大后验估计
采样(信号处理)
算法
脉冲响应
信号处理
先验与后验
人工智能
统计
数学
数字信号处理
最大似然
电信
探测器
认识论
数学分析
哲学
计算机硬件
作者
Petre Stoica,Xiaolei Shang,Yuanbo Cheng
出处
期刊:IEEE Signal Processing Magazine
[Institute of Electrical and Electronics Engineers]
日期:2022-09-01
卷期号:39 (5): 85-92
被引量:2
标识
DOI:10.1109/msp.2022.3177872
摘要
Any data modeling exercise has two main components: parameter estimation and model selection. The latter will be the topic of this lecture note. More concretely we will introduce several Monte-Carlo sampling-based rules for model selection using the maximum a posteriori (MAP) approach. Model selection problems are omnipresent in signal processing applications: examples include selecting the order of an autoregressive predictor, the length of the impulse response of a communication channel, the number of source signals impinging on an array of sensors, the order of a polynomial trend, the number of components of a NMR signal, and so on.
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