Concept
Bayesian programming
Related publications (4)
Learning in Volatile Environments With the Bayes Factor Surprise
Wulfram Gerstner, Johanni Michael Brea, Alireza Modirshanechi, Vasiliki Liakoni
Surprise-based learning allows agents to rapidly adapt to nonstationary stochastic environments characterized by sudden changes. We show that exact Bayesian inference in a hierarchical model gives rise to a surprise-modulated trade-off between forgetting o ...
MIT PRESS2021Quantifying Interdependent Privacy Risks with Location Data
Jean-Pierre Hubaux, Mathias Jacques Jean-Marc Humbert, Kévin Clément Huguenin, Reza Shokri, Alexandra-Mihaela Olteanu
Co-location information about users is increasingly available online. For instance, mobile users more and more frequently report their co-locations with other users in the messages and in the pictures they post on social networking websites by tagging the ...
Ieee Computer Soc2017Efficient Peer-to-Peer Belief Propagation
In this paper, we will present an efficient approach for distributed inference. We use belief propagation's message-passing algorithm on top of a DHT storing a Bayesian network. Nodes in the DHT run a variant of the spring relaxation algorithm to redistrib ...
2006An Adaptive Algorithm for Efficient Message Diffusion in Unreliable Environments
Rodrigo Malta Schmidt, Benoît Garbinato, Fernando Pedone
In this paper, we propose a novel approach for solving the reliable broadcast problem in a probabilistic model, i.e., where links lose messages and where processes crash and recover probabilistically. Our approach consists in first defining the optimality ...
2004