Bayesian statistics has been around for more than 250 years now. During this time it has enjoyed as much recognition and appreciation as disdain and contempt. Through the last few decades it has gained more and more attention from people in statistics and almost all other sciences, engineering, and even outside the walls of the academic world. This revival has been possible due to theoretical and computational developments. Modern Bayesian statistics is mostly computational statistics. The necessity for exible and transparent models and a more interpretation of statistical analysis has only contributed to the trend. Here, we will adopt a pragmatic approach to Bayesian statistics and we will not care too much about other statistical paradigms and their relationship to Bayesian statistics. The aim of this book is to learn about Bayesian data analysis with the help of Python. Philosophical discussions are interesting but they have already been undertaken elsewhere in a richer way than we can discuss in these pages. We will take a modeling approach to statistics, we will learn to think in terms of probabilistic models, and apply Bayes' theorem to derive the logical consequences of our models and data. The approach will also be computational; models will be coded using PyMC3—a great library for Bayesian statistics that hides most of the mathematical details and computations from the user. Bayesian methods are theoretically grounded in probability theory and hence it's no wonder that many books about Bayesian statistics are full of mathematical formulas requiring a certain level of mathematical sophistication. Learning the mathematical foundations of statistics could certainly help you build better models and gain intuition about problems, models, and results. Nevertheless, libraries, such as PyMC3 allow us to learn and do Bayesian statistics with only a modest mathematical knowledge, as you will be able to verify by yourself throughout this book.

在可视化设计过程中，时空数据的分析呈现是一个大问题，本论文提供了一个很好地解决方法，可供参考学习~~

关于微带天线的论文合集，由两位天线领域权威专家-David Pozar以及Daniel Schaubert-编写，两位教授来自马萨诸塞大学阿默斯特分校。

数值分析Numerical Analysis, Sauer著第3版的习题答案集，315页，虽然答案有步骤但感觉不是很详细，悟性不大的话看不太懂。另外Sauer的数值分析第1版国内有中文版，但到后面几章就有些机翻的味道了，虽然几种数值分析的教材里Sauer的教材最浅显易懂，覆盖面还大，但中文版动不动就有错误，建议中英文对照着看。

第三版

The Cadence:registered: Allegro:registered: system interconnect design platform enables collaborative design of high-performance interconnect across IC, package, and PCB domains. The platform's constraint-driven flow and co-design methodology optimizes system interconnect between I/O buffers and across ICs, packages, and

 Analysis III-Herbert Amann, Joachim Escher.pdf

实用的时间序列分析 这是出版的《 的代码库。 它包含从头到尾完成本书所必需的所有支持项目文件。 关于这本书 时间序列分析使我们能够分析一段时间内的某些数据并了解数据随时间变化的模式，这本书将使您了解时间序列分析背后的逻辑并将其应用于各个领域，包括财务，业务和社交媒体。 说明和导航 所有代码都组织在文件夹中。 每个文件夹均以数字开头，后跟应用程序名称。 例如，Chapter02。 该代码将如下所示： import os import pandas as pd %matplotlib inline from matplotlib import pyplot as plt import seaborn as sns 您将需要Anaconda Python发行版来运行本书中的示例，并编写自己的Python程序以进行时间序列分析。 可从免费下载。 本书的代码示例是使用Jupyter Noteb

芯片序列分析 Snakemake管道 我开发了一个基于Snakemake的ChIP-seq管道： 。 和ATACseq管道： ChIP-seq的资源 ： ：来自ENCODE的元数据的汇编。 一个bioc包，用于访问ENCODE的元数据并下载原始文件。 论文： 。 序列为.sra格式，需要使用sratools转储到fastq中。 。 序列以fastq格式提供。 用于核小体定位和TF ChIP-seq的工具和论文的集合 评论文章：解密ENCODE EpiFactors是一个表观遗传因子，相应的基因和产物的数据库。 生物明星手册。 我的ChIP-seq章节将于2017年4月发布！ ReMap 2018对法规区域的综合ChIP-seq分析。 ReMap地图集包含来自公共数据集的485个转录因子（TF），转录共激活因子（TCA）和染色质重塑因子（CRF）的8000万个峰。 可以浏览或

泛函分析讲义(MIT辅助教材)Functional analysis lecture notes by T.B. Ward，英文非扫描版