The rapidly growing amount and diversity of data are confronting us more than ever with the need to make informed predictions under uncertainty. The adverse impacts of climate change and natural hazards also motivate our search for reliable predictions. The range of statistical techniques that geomorphologists use to tackle this challenge has been growing, but rarely involves Bayesian methods. Instead, many geomorphic models rely on estimated averages that largely miss out on the variability of form and process. Yet seemingly fixed estimates of channel heads, sediment rating curves or glacier equilibrium lines, for example, are all prone to uncertainties. Neighbouring scientific disciplines such as physics, hydrology or ecology have readily embraced Bayesian methods to fully capture and better explain such uncertainties, as the necessary computational tools have advanced greatly. The aim of this article is to introduce the Bayesian toolkit to scientists concerned with Earth surface processes and landforms, and to show how geomorphic models might benefit from probabilistic concepts. I briefly review the use of Bayesian reasoning in geomorphology, and outline the corresponding variants of regression and classification in several worked examples. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd

中文翻译：

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贝叶斯地貌

快速增长的数据量和多样性使我们比以往任何时候都面临着在不确定性下做出明智预测的需求。气候变化和自然灾害的不利影响也促使我们寻求可靠的预测。地貌学家用来应对这一挑战的统计技术范围不断扩大，但很少涉及贝叶斯方法。取而代之的是，许多地貌模型依赖于估计的平均值，而这些平均值在很大程度上忽略了形式和过程的可变性。但是，例如，看似固定的河床水头，沉积物额定曲线或冰川平衡线的估计都容易产生不确定性。邻近的科学学科（例如物理学，水文学或生态学）已经很容易地采用贝叶斯方法来完全捕获并更好地解释此类不确定性，因为必要的计算工具已经大大提高。本文的目的是向关注地球表面过程和地形的科学家介绍贝叶斯工具包，并展示地貌模型如何从概率概念中受益。我简要回顾了贝叶斯推理在地貌学中的使用，并在几个工作示例中概述了回归和分类的相应变体。©2020作者。约翰·威利父子有限公司出版的《地球表面过程和地形》并在几个工作示例中概述了回归和分类的相应变体。©2020作者。约翰·威利父子有限公司出版的《地球表面过程和地形》并在几个工作示例中概述了回归和分类的相应变体。©2020作者。约翰·威利父子有限公司出版的《地球表面过程和地形》