貝氏定理是廣為人知的統計方法，用於探索未知的不確定性。而事前機率與事後機率是其中非常重要的概念。一般而言，我們認為事前機率 (Prior Probability) 發生在前，事後機率 (Posterior Probability) 發生在後。不過，似乎很少學者注意到事前機率 (Prior Probability) 與事後機率 (Posterior Probability) 的先後順序，是透過個人認知先後的視角或者為事件發生先後的視角來談論這項議題。若以後者來看事前 (Prior) 與事後機率 (Posterior Probability)，又會對貝氏定理造成什麼樣的影響。
為此，本文透過文獻回顧，理解過去學者在談論的事前機率 (Prior Probability) 與事後機率 (Posterior Probability) 為何？我們發現，學者們主要著墨在事前機率的主客觀性，而鮮少提及時間的概念會如何影響貝氏定理。我們期望透過明確區分個人認知先後與真正時間先後的視角，增進對貝氏定理的看法。
因此，本研究在貝氏定理中加入時間軸 (Timeline) 與因果性 (Causation) 兩個新元素，並且提出一個機率遞增法則 (Law of Increasing Probability, LIP) 的新理論。我們沒有改變貝氏定理的本質，只是加入新元素後，發現一個從來沒有注意到的有趣現象。而，我們也透過擬合可信來源的資料，加以驗證 LIP 的通用性。最後，我們總結 LIP 所帶來的理論與實務貢獻，希望有助於擴大學術界與實務界對貝氏定理的理解與應用。

Bayes’ theorem is a widely known statistical method for exploring unknown uncertainties. The prior and posterior probabilities are very important concepts. In General, we think that prior probability occurs before and posterior probability occurs after. However, it seems that few scholars have paid attention to the order of prior and posterior probability, and they discuss this issue through the perspective of individual cognition or the perspective of event occurrence. If we look at prior and posterior probability from the latter perspective, what are the implications for Bayes' theorem?
In this paper, we review the literature to understand what scholars have been talking about prior and posterior probability. We find that scholars have mainly focused on the subjectivity of prior probability, but rarely mentioned how the concept of time affects Bayes' theorem. We hope to have a better understanding of Bayes' theorem by explicitly distinguishing between the perspective of individual cognition and that of true temporal events happen.
Therefore, in this study, we add two new elements to Bayes' theorem, namely, timeline and causation and propose a new theory of Law of Increasing Probability, LIP. We do not change the essence of Bayes' theorem, but we find an interesting phenomenon that has never been noticed before by adding the new elements. In addition, we verify the generality of LIP by fitting data from credible sources. Finally, we summarize the theoretical and practical contributions of LIP, which hopefully will help to expand the understanding and application of Bayes’ theorem to the academic and the industry.

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