We study the asymptotic learning rates of belief vectors in a distributed hypothesis testing
problem under linear and log-linear combination rules. We show that under both …

Traditional social learning frameworks consider environments with a homogeneous state,
where each agent receives observations conditioned on that true state of nature. In this …

This work provides a comprehensive overview of adaptive diffusion networks, from the first
papers published on the subject to state-of-the-art solutions and current challenges. These …

The adaptive social learning paradigm helps model how networked agents are able to form
opinions on a state of nature and track its drifts in a changing environment. In this framework …

Traditional social learning frameworks consider environments with a homogeneous state
where each agent receives observations conditioned on the same hypothesis. In this work …

This work studies networked agents cooperating to track a dynamical state of nature under
partial information. The proposed algorithm is a distributed Bayesian filtering algorithm for …

Doeblin coefficients are a classical tool for analyzing the ergodicity and exponential
convergence rates of Markov chains. Propelled by recent works on contraction coefficients of …

In social learning, a group of agents linked by a graph topology collect data and exchange
opinions on some topic of interest, represented by a finite set of hypotheses. Traditional …

Social learning algorithms provide a model for the formation and propagation of opinions
over social networks. However, most studies focus on the case in which agents share their …

This work examines a social learning problem, where dispersed agents connected through
a network topology interact locally to form their opinions (beliefs) as regards certain …