本文考慮伽瑪隨機過程之階段應力加速衰退試驗(SSADT)之貝氏可靠度分析。在加速因子為溫度之Arrhenius模型下,以主觀先驗分佈經由馬可夫鏈蒙地卡羅方法(MCMC) 得在常溫下產品壽命及可靠度之貝氏推論。另一方面,藉由在類似產品置於正常環境應力水準下之序列衰退試驗中,更新先驗分佈之超參數,以預測產品失效時間之分佈,同時決定測試時間,並以模擬資料驗 證所提方法的可行性和準確性以及貝氏方法之穩健性。;Degradation analysis is more efficient than the conventional life tests in drawing reliability assessment for high quality products. This thesis aims on the Bayesian approach to the degradation test when the degradation data of different products are collected under higher than normal stress levels via independent gamma processes. Reliability inference of the population under normal condition will be made based on the posterior distribution of the underlying parameters with the aid of Markov chain Monte Carlo method. Further sequentially predictive inference on individual reliability under normal condition is also proposed. Simulation study is presented to show the appropriateness of the proposed methods, and the robustness of the prior distribution.