臺灣博碩士論文加值系統

本文以經驗貝氏的方法分析台灣東部花蓮附近地區的地震資料。地震發生的時間與其規模為一點過程，我們沿用 Ogata (1988) 的觀念，在地震規模已知的條件下，以一個流行病模型 (epidemic-type model) 做為發生地震時間之條件強度函數 (conditional intensity function)，此一模型由兩部份組成，一是主震對發震率的影響，另一是餘震對發震率之影響。先利用早期資料決定此一模型參數之先驗分配 (prior distribution)。再利用馬可夫鏈蒙地卡羅 (Markov chain Monte Carlo) 法衍生近似的聯合後驗分配進行貝氏推論與分析，再經過適當的轉換後，提供一個預測地震的方法。

第一章 緒論{1}
1.1 文獻與方法回顧{1}
1.2 研究方法{2}
第二章 貝氏分析{4}
2.1 概似函數{4}
2.2 先驗分配{6}
2.2.1 指定先驗分配的形式{6}
2.2.2 統計計算方法{10}
2.2.3 先驗分配中超參數的決定{11}
2.3 後驗分配{14}
第三章 模型診斷及預測{20}
3.1 模型選擇{20}
3.2 模型診斷{21}
3.3 地震預測{22}
第四章 結論與展望{27}
參考文獻{28}

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