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題 名 | A Proposed Bayesian Inference Framework and the Property of the Likelihood Function=貝氏推論架構與概似函數特性之研究 |
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作 者 | 王鴻儒; 簡禎富; | 書刊名 | 管理與系統 |
卷 期 | 7:3 2000.07[民89.07] |
頁 次 | 頁305-325 |
分類號 | 319.5 |
關鍵詞 | 貝氏推測模式; 概似函數; 統計決策分析; 系統模式; 決策分析; Bayesian inference model; Likelihood function; Statistical decision making; System modeling; Decision analysis; |
語 文 | 英文(English) |
中文摘要 | 貝氏推論在抽樣資訊不足時,如何利用先驗機率模式與概似函數來計算未來風險。雖已有相關學者針對先驗機率與概似函數之分配機率做不同之組合,以求取事後機率之共軛性,但仍舊未有針對資料本身之特質做適當之配合處理。本研究即針對資料蒐集之性質,尋求適當之先驗機率與概似函數,進而找出分析架構,來使資料與貝氏推論模式相符合。最後,針對卜氏過程與白努力過程驗證本研究提出之貝氏推論架構與概似函數特性,並以實例說明。 |
英文摘要 | The Bayesian approach, which allows effective use of prior information, is especially useful in the situation that the sample evidence is insufficient. Researchers have used different probability distribution functions to express the prior distributions and the likelihood functions in various Bayesian inference models. However, little research has been done to examine the nature of different problems and thus select sutiable likelihood functions. To respond to this research need, we proposed a Bayesian inference framework. For demonstration, we showed that, when a specific event (e.g., accident of failure) in a system occurs as a binomial process or Poisson process, the Bayesian inference model on the event rate will have the likelihood function as a binomial distribution or Poisson distribution, respectively. |
本系統中英文摘要資訊取自各篇刊載內容。