摘要翻译：
可逆跳跃马尔可夫链蒙特卡罗(RJMCMC)是一种用于跨维抽样的贝叶斯模型估计方法。在本研究中，我们提出利用RJMCMC超越跨维抽样。这种新的解释，我们称之为跨空间RJMCMC，揭示了RJMCMC未被发现的潜力，利用原来的公式来探索不同类别或结构的空间。这提供了在组合模型空间中使用不同类型的候选类的灵活性，例如线性和非线性模型的空间或各种分布族的空间。作为该方法的一个应用，我们在脉冲数据建模中实现了跨空间采样的一个特例，即跨分布RJMCMC。在地震学、雷达、图像等许多领域，由于分析容易，使用高斯模型是一种常见的做法。然而，许多噪声过程并不遵循高斯特性，通常表现出太冲动的事件，无法成功地用高斯模型描述。我们测试了在各种脉冲分布族中进行选择的方法，以模拟综合产生的噪声过程和电力线通信(PLC)脉冲噪声和二维离散小波变换系数的实际测量。
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英文标题：
《Beyond trans-dimensional RJMCMC with a case study in impulsive data
  modeling》
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作者：
Oktay Karaku\c{s}, Ercan E. Kuruo\u{g}lu, Mustafa A. Alt{\i}nkaya
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最新提交年份：
2020
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  Reversible jump Markov chain Monte Carlo (RJMCMC) is a Bayesian model estimation method which has been used for trans-dimensional sampling. In this study, we propose utilization of RJMCMC beyond trans-dimensional sampling. This new interpretation, which we call trans-space RJMCMC, reveals the undiscovered potential of RJMCMC by exploiting the original formulation to explore spaces of different classes or structures. This provides flexibility in using different types of candidate classes in the combined model space such as spaces of linear and nonlinear models or of various distribution families. As an application for the proposed method, we have performed a special case of trans-space sampling, namely trans-distributional RJMCMC in impulsive data modeling. In many areas such as seismology, radar, image, using Gaussian models is a common practice due to analytical ease. However, many noise processes do not follow a Gaussian character and generally exhibit events too impulsive to be successfully described by the Gaussian model. We test the proposed method to choose between various impulsive distribution families to model both synthetically generated noise processes and real-life measurements on power line communications (PLC) impulsive noises and 2-D discrete wavelet transform (2-D DWT) coefficients.
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PDF链接：
https://arxiv.org/pdf/1711.03633