センサネットワークにおける分散ベイズ推定：周辺密度の合意形成
Distributed Bayesian Estimation in Sensor Networks: Consensus on Marginal Densities
センサネットワークで非線形尤度を持つデータから分散ベイズ推定を行うアルゴリズムを提案し、各エージェントが観測する変数のみを推定するストレージ効率の良い手法を開発した。
著者: Parth Paritosh, Nikolay Atanasov, Sonia Martinez
分類: cs.LG, cs.MA, cs.RO, eess.SP
原文アブストラクト
In this paper, we aim to design and analyze distributed Bayesian estimation algorithms for sensor networks. The challenges we address are to (i) derive a distributed provably-correct algorithm in the functional space of probability distributions over continuous variables, and (ii) leverage these results to obtain new distributed estimators restricted to subsets of variables observed by individual agents. This relates to applications such as cooperative localization and federated learning, where the data collected at any agent depends on a subset of all variables of interest. We present Bayesian density estimation algorithms using data from non-linear likelihoods at agents in centralized, distributed, and marginal distributed settings. After setting up a distributed estimation objective, we prove almost-sure convergence to the optimal set of pdfs at each agent. Then, we prove the same for a storage-aware algorithm estimating densities only over relevant variables at each agent. Finally, we present a Gaussian version of these algorithms and implement it in a mapping problem using variational inference to handle non-linear likelihood models associated with LiDAR sensing.
