We study a model of social learning, in which a group of. Bayesian agents learn a state of the world through repeated interaction with their social network ...
Feb 3, 2010 · Abstract:We consider a group of Bayesian agents who try to estimate a state of the world \theta through interaction on a social network.
It is shown that on trees and on distance-transitive graphs the process converges after D steps, and that it preserves privacy, so that agents learn very ...
Apr 20, 2010 · We propose a Bayesian model of iterative learning on social networks that is computationally tractable; the agents of this model are fully ...
We propose a Bayesian model of iterative learning on social networks that is computationally tractable; the agents of this model are fully rational, ...
Sep 27, 2016 · We consider a group of Bayesian agents who try to estimate a state of the world θ through interaction on a social network.
This motivates our development of a divide-and-conquer method that can learn massive-size Bayesian networks efficiently and accurately. Our method consists of ...
This article proposes a Bayesian computing algorithm to infer Gaussian directed acyclic graphs (DAG's). It has the ability of escaping local modes and ...
Missing: Social | Show results with:Social
Duration: 57:16
Posted: Oct 27, 2022
Posted: Oct 27, 2022
Missing: Efficient Gaussian Estimators
In this case, we use efficient learning, which requires the agents' estimates in the long run to match (the log-likelihood ratios of) the Bayesian posterior.