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親愛的您，
這裡是LearningYard新學苑！
今天小編給大家帶來博士論文解讀，
歡迎您的用心通路！
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!
今天小編将為大家帶來博士論文《基于可信性度量的SaaS服務選擇與演化方法的研究》相關理論基礎的内容，接下來我們開始今天的學習吧！
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!
“
思維導圖
下圖是本節内容的思維導圖：
The following figure is the mind map of this section:
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精讀内容
本文所涉及的相關理論和方法有很多種，第三章節主要對這些概念和方法等内容進行詳細介紹。
There are many theories and methods involved in this article, and Chapter 3 mainly provides a detailed introduction to these concepts and methods.
首先是資訊熵理論。熵的理論被引入到資訊論中，是用來衡量資訊量的大小，定義為資訊熵。
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.
其次是馬爾可夫相關理論。具體包含馬爾可夫鍊理論和隐馬爾可夫模型概念，針對馬爾可夫鍊理論，作者定義了馬爾可夫過程、馬爾可夫鍊、馬爾可夫鍊的h步轉移機率和齊次馬爾可夫鍊等概念。
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.
最後針對隐馬爾可夫模型進行描述，這是對馬爾可夫鍊理論的發展和延伸，作者詳細介紹了該模型的内涵和結構。
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.
今天的分享就到這裡了。
如果您對今天的文章有獨特的想法，
歡迎給我們留言，
讓我們相約明天，
祝您今天過得開心快樂！
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!
參考資料：ChatGPT翻譯，百度百科
參考文獻：
[1] 高提雷. 基于可信性度量的SaaS服務選擇與演化方法的研究 [D]. 雲南: 雲南大學, 2020.
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文案 |Yuan
排版 |Yuan
稽核 |Wang