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：

---

具有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。代理试图共同识别最能描述观察序列的潜在目标类别。首先将问题抽象到假设检验框架，在该假设框架中，我们假设代理人寻求就与观察分布最匹配的假设（目标类别）达成共识。非贝叶斯社会学习理论提供了一种框架，该框架通过允许代理通过网络顺序地交流和更新其对每种假设的信念，从而以有效的方式解决了这一问题。大多数现有方法都假定代理人可以使用每种假设的精确统计模型。但是，在许多实际应用中，代理会根据有限的数据学习似然模型，这引起了似然函数参数的不确定性。在这项工作中，我们基于不确定性模型的概念，通过确定一组广泛的参数分布，使代理商的信念收敛到与集中式方法相同的结果，从而将代理商的不确定性纳入可能性。此外，我们根据经验探索对非参数模型的扩展，以提供非贝叶斯社会学习中不确定模型的通用框架。