部分因子實驗在科學界與業界的研發常被使用，而使用部分因子設計必然會使得因子效應之間產生混淆。在文獻上，效應混淆的議題在貝氏設計與電腦實驗中常使用的高斯隨機域模型下並未受到探討。此論文提出一高斯隨機域的線性結構，並在此結構下，分別對質性因子與量性因子探討高斯隨機域模型中效應混淆的議題。我們建立了高斯隨機域中效應的概念，並提出一衡量效應混淆嚴重程度的指標。此指標除了可呈現效應混淆的資訊外，亦涵蓋了模型複雜度的概念。我們也探討了效應混淆對模型一些統計性質上的影響，像是參數估計、效應的貝氏後驗共變異數矩陣以及預測變異數。

Effect aliasing is an inevitable consequence of using fractional factorial designs. For Gaussian random field models, advocated in some Bayesian design and computer experiment literature, the impact of effect aliasing has not received adequate attention. In this dissertation,
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