Utility-Based Learning from Data (Hardcover)

Utility-Based Learning from Data provides a pedagogical, self-contained discussion of probability estimation methods via a coherent approach from the viewpoint of a decision maker who acts in an uncertain environment. This approach is motivated by the idea that probabilistic models are usually not learned for their own sake; rather, they are used to make decisions. Specifically, the authors adopt the point of view of a decision maker who

(i) operates in an uncertain environment where the consequences of possible outcomes are explicitly monetized,
(ii) bases his decisions on a probabilistic model, and
(iii) builds and assesses his models accordingly.

These assumptions are naturally expressed in the language of utility theory, which is well known from finance and decision theory. By taking this point of view, the book sheds light on and generalizes some popular statistical learning approaches, connecting ideas from information theory, statistics, and finance. It strikes a balance between rigor and intuition, conveying the main ideas to as wide an audience as possible.

《基於效用的數據學習》提供了一個教學性、自成體系的討論，通過一種從不確定環境中行動的決策者的觀點，來介紹概率估計方法。這種方法的動機是基於概率模型通常不是為了自身學習而學習的想法；相反，它們被用於做出決策。具體而言，作者們採用了一個決策者的觀點：

(i) 在後果明確貨幣化的不確定環境中運作，
(ii) 基於概率模型做出決策，
(iii) 構建和評估他的模型。

這些假設在效用理論的語言中自然表達出來，效用理論在金融和決策理論中是眾所周知的。通過採取這種觀點，本書闡明並概括了一些流行的統計學習方法，將信息理論、統計學和金融的思想聯繫起來。它在嚴謹性和直觀性之間取得平衡，以便向盡可能廣泛的讀者傳達主要思想。