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Hidden Markov Modeling over Graphs
TLDR
In this article, a multi-agent filtering algorithm over graphs for finite-state hidden Markov models (HMMs) is proposed, which can be used for sequential state estimation or for tracking opinion formation over dynamic social networks.Citations
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Policy Evaluation in Decentralized POMDPs With Belief Sharing
TL;DR: In this article , a decentralized belief forming strategy that relies on individual updates and on localized interactions over a communication network is proposed, where agents exploit the communication network by exchanging value function parameter estimates as well.
Proceedings ArticleDOI
Online Deep Knowledge Tracing
TL;DR: Zhang et al. as mentioned in this paper proposed an online deep knowledge tracing model, dubbed ODKT, by utilizing the online gradient descent algorithm to develop the traditional DKT into online learning.
References
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Equation of state calculations by fast computing machines
TL;DR: In this article, a modified Monte Carlo integration over configuration space is used to investigate the properties of a two-dimensional rigid-sphere system with a set of interacting individual molecules, and the results are compared to free volume equations of state and a four-term virial coefficient expansion.
Proceedings ArticleDOI
Distributed Kalman filtering for sensor networks
TL;DR: A continuous-time distributed Kalman filter that uses local aggregation of the sensor data but attempts to reach a consensus on estimates with other nodes in the network and gives rise to two iterative distributedKalman filtering algorithms with different consensus strategies on estimates.
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Diffusion Strategies for Distributed Kalman Filtering and Smoothing
TL;DR: This work studies the problem of distributed Kalman filtering and smoothing, and proposes diffusion algorithms to solve each one of these problems, and compares the simulation results with the theoretical expressions, and notes that the proposed approach outperforms existing techniques.
Journal ArticleDOI
Bayesian Learning in Social Networks
TL;DR: The main theorem shows that when the probability that each individual observes some other individual from the recent past converges to one as the social network becomes large, unbounded private beliefs are sufficient to ensure asymptotic learning.
Book
Adaptation, Learning, and Optimization Over Networks
TL;DR: The limits of performance of distributed solutions are examined and procedures that help bring forth their potential more fully are discussed and a useful statistical framework is adopted and performance results that elucidate the mean-square stability, convergence, and steady-state behavior of the learning networks are derived.