贝叶斯概率
透视图(图形)
集合(抽象数据类型)
贝叶斯推理
计算机科学
开发(拓扑)
情感(语言学)
贝叶斯统计
数据科学
人工智能
心理学
数学
沟通
数学分析
程序设计语言
作者
Judy A. Stamps,Willem E. Frankenhuis
标识
DOI:10.1016/j.tree.2016.01.012
摘要
Until recently, biology lacked a framework for studying how information from genes, parental effects, and different personal experiences is combined across the lifetime to affect phenotypic development. Over the past few years, researchers have begun to build such a framework, using models that incorporate Bayesian updating to study the evolution of developmental plasticity and developmental trajectories. Here, we describe the merits of a Bayesian approach to development, review the main findings and implications of the current set of models, and describe predictions that can be tested using protocols already used by empiricists. We suggest that a Bayesian perspective affords a simple and tractable way to conceptualize, explain, and predict how information combines across the lifetime to affect development.
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