The Normalizing Constant in the BG/BB Model

D. McCarthy, Michael Braun, Arun Gopalakrishnan
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引用次数: 0

Abstract

This note provides a clarification regarding the conditional and marginal likelihood functions in the BG/BB model, as published in Marketing Science by Fader, Hardie, and Shang (2010). Their Equations 4 and 5 do not include normalizing constants which, if included, would equate these likelihood functions with their corresponding joint probability functions. While these expressions are valid, because likelihood functions need only be correct up to a constant of proportionality, they are not joint probability functions, which may be a source of potential confusion for users who mistakenly equate the one for the other. Assuming the likelihood functions in Equations 4 and 5 are equal to their respective joint probability functions will lead to an incorrect joint probability distribution over recency and frequency data, resulting in incorrect goodness-of-fit metrics and managerially relevant expressions. We provide formal derivations of the joint probability functions that correspond to the likelihood functions in Equations 4 and 5 to remove this potential source of confusion for users of the BG/BB model.
BG/BB模型的归一化常数
本文对Fader、Hardie和Shang(2010)在《市场营销科学》上发表的BG/BB模型中的条件似然函数和边际似然函数进行了澄清。他们的方程4和5不包括归一化常数,如果包括这些归一化常数,将使这些似然函数与相应的联合概率函数相等。虽然这些表达式是有效的,因为似然函数只需要在比例常数范围内是正确的,但它们不是联合概率函数,这可能会给错误地将两者等同起来的用户造成潜在的混淆。假设式4和式5中的似然函数等于其各自的联合概率函数,将导致对近时性和频度数据的联合概率分布不正确,从而导致拟合优度指标和管理相关表达式不正确。我们提供了与公式4和5中的似然函数相对应的联合概率函数的形式化推导,以消除BG/BB模型用户的这种潜在混淆来源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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