深入浅出面向对象的分析与设计Head.First.Object-Oriented.Design.and.Analysis这本书以较轻快的语气为我们讲述了面向对象的分析与设计，适合精读，多读，仔细揣摩其中的思想

The book's conceptual presentation focuses on ADTs and the analysis of algorithms for efficiency, with a particular concentration on performance and running time.

MALDI-TOF-MS分析小分子用修饰的介孔材料SBA-15作为基质，李秀花，吴学，本论文中成功的将喹啉修饰到介孔材料SBA-15上，并且将其作为基质用于MALDI-TOF-MS中分析小分子。修饰化的SBA-15(SBA-15-8-(3-三甲氧基硅烷基�

Density Estimation for Statistics and Data Analysis， Silverman著， 1986年版，核密度估计教材

Radar Systems Analysis and Design Using MATLAB，最新第四版 2022年

Applying UML and Patterns An Introduction to Object-Oriented Analysis and Design and Iterative Development (3rd Edition).pdf UML经典教材

ethod while keeping a balanced approach between its mathematical formulation, programming implementation and as its application using commercial software. The computer implementation is carried out using MATLAB, while the practical applications are carried out in both MATLAB and Abaqus. MATLAB is a high-level language specially designed for dealing with matrices, making it particularly suited for programming the finite element method. In addition, it also allows the reader to focus on the finite element method by alleviating the programming burden. Experience has shown that books that include programming examples that can be implemented are of benefit to beginners. This book also includes detailed step-by-step procedures for solving problems with Abaqus interactive and keyword editions.

陶哲轩的数学分析

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~~