Video-Compression-motion-estimation-block-video-encoder：此存储库与视频压缩有关，更具体地说，与视频编码器的运动估计块（ME块）有关。 这是一个研究项目，旨在开发一种有效的运动估计算法，从而使视频压缩技术能够与高帧率视频和高分辨率视频保持同步。

双选信道的各种方法，有常见的一些LS，MMSE和OMP算法，还有一张对比图

State Estimation for Robotics_简介-附件资源

DINA模型及其参数估计，作者Jimmy de la Torre，发表于2009年，引用数为：385

This textbook evolved from a course in geophysical inverse methods taught during the past two decades at New Mexico Tech, first by Rick Aster and, subsequently, jointly between Rick Aster and Brian Borchers. The audience for the course has included a broad range of first- or second-year graduate students (and occasionally advanced under- graduates) from geophysics, hydrology, mathematics, astrophysics, and other disciplines. Cliff Thurber joined this collaboration during the production of the first edition and has taught a similar course at the University of Wisconsin-Madison. Our principal goal for this text is to promote fundamental understanding of param- eter estimation and inverse problem philosophy and methodology, specifically regarding such key issues as uncertainty, ill-posedness, regularization, bias, and resolution. We emphasize theoretical points with illustrative examples, and MATLAB codes that imple- ment these examples are provided on a companion website. Throughout the examples and exercises, a web icon indicates that there is additional material on the website. Exercises include a mix of applied and theoretical problems. This book has necessarily had to distill a tremendous body of mathematics and science going back to (at least) Newton and Gauss. We hope that it will continue to find a broad audience of students and professionals interested in the general problem of estimating physical models from data. Because this is an introductory text surveying a very broad field, we have not been able to go into great depth. However, each chapter has a “notes and further reading” section to help guide the reader to further explo- ration of specific topics. Where appropriate, we have also directly referenced research contributions to the field. Some advanced topics have been deliberately left out of this book because of space limitations and/or because we expect that many readers would not be sufficiently famil- iar with the required mathematics. For example, readers with a strong mathematical background may be surprised that we primarily consider inverse problems with discrete data and discretized models. By doing this we avoid much of the technical complexity of functional analysis. Some advanced applications and topics that we have omitted include inverse scattering problems, seismic diffraction tomography, wavelets, data assimilation, simulated annealing, and expectation maximization methods. We expect that readers of this book will have prior familiarity with calculus, dif- ferential equations, linear algebra, probability, and statistics at the undergraduate level. In our experience, many students can benefit from at least a review of these topics, and we commonly spend the first two to three weeks of the course reviewing material from

We live in a new age for statistical inference, where modern scientific technology such as microarrays and fMRI machines routinely produce thousands and sometimes millions of parallel data sets, each with its own estimation or testing problem. Doing thousands of problems at once is more than repeated application of classical methods. Taking an empirical Bayes approach, Bradley Efron, inventor of the bootstrap, shows how information accrues across problems in a way that combines Bayesian and

惩罚样条估计 （对application.txt的R代码的描述）这是论文中带有时变系数的面板计数数据模型的罚样条估计的应用R代码。 通过运行此代码，我们可以获得公式（7）的惩罚样条估计。 该代码可轻松适用于其他面板计数数据模型。 可以在application.txt文件的R代码中找到每个功能的注释。 （对sample_data.csv的描述）这是上述R代码的示例数据集，可以在读取示例数据集后直接运行。 可以通过向我发送电子邮件至来提供本文中用于仿真的R代码。

RRI_estimation MATLAB：从原始 ECG 数据生成 RR 间期 (RRI) 数据 心输出量通常以心电图的形式记录，其中包含几个清晰可辨的峰值。 特别是，R 峰值是波形中的主要尖峰。 大多数分析是在从 ECG 得出的时间序列上进行的——RR 间期 (RRI)——它是连续 R 峰值之间的时间差。 RRI通过以下步骤获得： 第 1 步：心电图经过带通滤波（建议范围 5 - 20 Hz） 步骤 2：估计 R 峰值。 R 峰值预计大于参数“ampthresh”，并且预计在连续峰值“timethresh”之间存在最小时间间隔。 第 3 步：用户将看到一个图，说明已识别的峰（上部子图）和连续峰之间的时间差（下部子图）。 然后提示用户： Change parameters (Y/N) ? 输入“Y”允许用户在继续下一步之前更改参数。 如果只有少数峰被错误分类（异

阵列信号处理中用于波达方向估计的TAM算法

贝叶斯估计，也是卡尔曼滤波算法的基础 是一篇很好的文章