分享兴趣，传播快乐，增长见闻，留下美好。
亲爱的您，
这里是LearningYard新学苑！
今天小编给大家带来博士论文解读，
欢迎您的用心访问！
Share interest, spread happiness, increase knowledge, and leave beautiful.
Dear, this is the LearingYard Academy!
Today, the editor will bring you a a doctoral dissertation analysis.
Welcome to visit!
今天小编将为大家带来博士论文《基于可信性度量的SaaS服务选择与演化方法的研究》相关理论基础的内容，接下来我们开始今天的学习吧！
Today, the editor will bring you the theoretical basis of the doctoral thesis "Research on SaaS Service Selection and Evolution Methods Based on Credibility Measurement". Let's start today's learning!
“
思维导图
下图是本节内容的思维导图：
The following figure is the mind map of this section:
“
精读内容
本文所涉及的相关理论和方法有很多种，第三章节主要对这些概念和方法等内容进行详细介绍。
There are many theories and methods involved in this article, and Chapter 3 mainly provides a detailed introduction to these concepts and methods.
首先是信息熵理论。熵的理论被引入到信息论中，是用来衡量信息量的大小，定义为信息熵。
Firstly, there is the theory of information entropy. The theory of entropy was introduced into information theory to measure the amount of information, defined as information entropy.
其次是马尔可夫相关理论。具体包含马尔可夫链理论和隐马尔可夫模型概念，针对马尔可夫链理论，作者定义了马尔可夫过程、马尔可夫链、马尔可夫链的h步转移概率和齐次马尔可夫链等概念。
Secondly, there are Markov related theories. Specifically, it includes the concepts of Markov chain theory and hidden Markov models. For Markov chain theory, the author defines concepts such as Markov processes, Markov chains, h-step transition probabilities of Markov chains, and homogeneous Markov chains.
最后针对隐马尔可夫模型进行描述，这是对马尔可夫链理论的发展和延伸，作者详细介绍了该模型的内涵和结构。
Finally, a description of the Hidden Markov Model is provided, which is an extension and development of Markov Chain Theory. The author provides a detailed introduction to the connotation and structure of this model.
今天的分享就到这里了。
如果您对今天的文章有独特的想法，
欢迎给我们留言，
让我们相约明天，
祝您今天过得开心快乐！
That's all for today's sharing.
If you have a unique idea about the article, please leave us a message, and let us meet tomorrow.
I wish you a nice day!
参考资料：ChatGPT翻译，百度百科
参考文献：
[1] 高提雷. 基于可信性度量的SaaS服务选择与演化方法的研究 [D]. 云南: 云南大学, 2020.
本文由LearningYard新学苑原创，如有侵权请在后台留言！
文案 |Yuan
排版 |Yuan
审核 |Wang