本研究我們使用和貝氏技術些微不同的方法，傳統的貝氏方法是假設有先驗分配(可能是共軛分配)和以超參數為中心。我們用小樣本中非常少的觀察值作資料增加擴大，用以觀察值(假設它們是超參數)為中心的先驗分配來產生一個較大的資料集(稱作第二產生資料集)，這個第二產生資料集可以用來做統計推論，這方法需要用到電腦資源而且是對應用問題有幫助的。

In this study we follow the method of Bayesian technique with a difference. In traditional Bayesian method one assumes a prior distribution, may be a conjugate one, which is centered at super parameters. Our approach of data increase is supposed to help for small samples or cases where a very few observations are available. We use prior distributions, centered at the given observations (pretending them to be super parameters), to generate a larger artificial dataset which may be termed as second generation dataset. This larger second generation dataset is then used to draw statistical inferences. The method is dependent on computational resources, and may be useful in applied problems.