这篇是个翻译稿，TODO
In part two of the Stateless Ethereum series, we explore our approach to building a model of such a complex ecosystem.
在无状态以太坊系列的第二部分，我们探讨了我们为这样一个复杂的生态系统建立模型的方法。
作为无状态以太坊系列的一部分，第一篇博客探讨了为什么提出无状态以太坊，以及无状态以太坊给以太坊生态系统带来的关键变化。第一部分还简要介绍了在模型设计中实现复杂性和简单性之间平衡的必要性。在本系列的第二部分中，我们将看看我们是如何在平衡这一需求的同时，为这样一个复杂的生态系统建立一个模型的。
以上需要调整
There are many different modeling techniques. However, we decided on a Bayesian Network (BN) modeling approach for several reasons.
First, a BN is a probabilistic graphical and visual modelling tool that represents the interaction of stakeholders within the model. Secondly, we can explicitly represent uncertainty using joint probability distributions across all the factors and interactions in the model. Thirdly, this network model is suited to modelling complex systems like Ethereum. And finally, it may be used regardless of whether there is a lot or little data. Diverse data sources can be combined to inform the model like expert knowledge, empirical data, model output, published and grey literature.
Perhaps the most compelling reason to use a Bayesian Network to model Ethereum is that it explicitly captures uncertainty, allowing the model to learn from new research and data input. The BN is designed to predict the health of the ecosystem after the introduction of Stateless Ethereum.
Figure 1 shows the simplified view of the complete model, consisting of four sub-models: Block creation, Witness creation, Ethereum network, and Block propagation.
Figure 1: Stateless Ethereum model showing four sub-models
In each of the sub-models, we capture the key factors and their interactions in Ethereum mainnet and focus on the changes introduced by Stateless Ethereum.
Once all the information has been added to the model, we are then able to explore how the model behaves when certain parts of it change. More specifically, we see to what extent these changes potentially impact on the overall health of the Ethereum ecosystem.
We use publicly available Ethereum mainnet data, added with other data sources that include model output and expert knowledge in order to quantify the model.