This paper studies the problem of distributed classification with a network of heterogeneous agents. The agents seek to jointly identify the underlying target class that best describes a sequence of observations. The problem is first abstracted to a hypothesis-testing framework, where we assume that the agents seek to agree on the hypothesis (target class) that best matches the distribution of observations. Non-Bayesian social learning theory provides a framework that solves this problem in an efficient manner by allowing the agents to sequentially communicate and update their beliefs for each hypothesis over the network. Most existing approaches assume that agents have access to exact statistical models for each hypothesis. However, in many practical applications, agents learn the likelihood models based on limited data, which induces uncertainty in the likelihood function parameters. In this work, we build upon the concept of uncertain models to incorporate the agents’ uncertainty in the likelihoods by identifying a broad set of parametric distribution that allows the agents’ beliefs to converge to the same result as a centralized approach. Furthermore, we empirically explore extensions to non-parametric models to provide a generalized framework of uncertain models in non-Bayesian social learning.

中文翻译：

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不确定模型的分布式推理的通用框架


本文研究了异构代理网络的分布式分类问题。代理寻求共同识别最能描述一系列观察结果的潜在目标类别。该问题首先被抽象为一个假设检验框架，在该框架中，我们假设代理寻求就最符合观察分布的假设（目标类别）达成一致。非贝叶斯社会学习理论提供了一个框架，通过允许代理通过网络顺序地沟通和更新他们对每个假设的信念，以有效的方式解决这个问题。大多数现有方法假设代理可以访问每个假设的精确统计模型。然而，在许多实际应用中，智能体基于有限的数据来学习似然模型，这会导致似然函数参数的不确定性。在这项工作中，我们以不确定模型的概念为基础，通过识别一组广泛的参数分布来将代理的不确定性纳入可能性中，该参数分布允许代理的信念收敛到与集中方法相同的结果。此外，我们凭经验探索非参数模型的扩展，以提供非贝叶斯社会学习中不确定模型的通用框架。