Asymptotic Optimality Theory for Decentralized Sequential Hypothesis Testing in Sensor Networks

Mei, Yajun

doi:10.1109/tit.2008.920217

Cited by 79 publications

(113 citation statements)

References 22 publications

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“…A more general formulation of the fusion problem was introduced by Hashemi & Rhodes [52], and a more complete solution to this problem was given by Veeravalli and co-workers [21,53]. Recent work includes an asymptotic theory for sequential hypothesis testing in sensor networks in the regime of small-error probabilities [54].…”

Section: (F ) Sequential and Quickest Change Detectionmentioning

confidence: 99%

Distributed inference in wireless sensor networks

Veeravalli

Varshney

2012

Phil. Trans. R. Soc. A.

109

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Statistical inference is a mature research area, but distributed inference problems that arise in the context of modern wireless sensor networks (WSNs) have new and unique features that have revitalized research in this area in recent years. The goal of this paper is to introduce the readers to these novel features and to summarize recent research developments in this area. In particular, results on distributed detection, parameter estimation and tracking in WSNs will be discussed, with a special emphasis on solutions to these inference problems that take into account the communication network connecting the sensors and the resource constraints at the sensors.

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Section: (F ) Sequential and Quickest Change Detectionmentioning

confidence: 99%

Distributed inference in wireless sensor networks

Veeravalli

Varshney

2012

Phil. Trans. R. Soc. A.

109

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“…For every π ∈ S m−1 , an optimal decision rule for the rewardrate maximization problem in (15) with s = 0 is given by…”

Section: Multihypothesis Sequential Testing: Reward Rate Maximizationmentioning

confidence: 99%

“…In neuro-science and psychology, humans [4] and animals [14] are often modeled as maximizing the long-run average reward rate, or the ratio of accuracy to expected temporal delay. In computer science and engineering modeling, the speed-accuracy trade-off is typically formalized in terms of Bayes-risk minimization, which minimizes a linear combination of expected temporal delay and response errors [18,16,10,11,15,9,8,12]. The advantage of the risk minimization formulation is that the linear speed-accuracy trade-off makes it amenable to a substantial body of tools for solving or characterizing the optimal solution, including Wald's sequential statistical decision formulation [17] and Bellman's dynamic programming principle [1].…”

Section: Introductionmentioning

confidence: 99%

Reward-Rate Maximization in Sequential Identification under a Stochastic Deadline

Dayanık¹,

Yu²

2013

SIAM J. Control Optim.

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Abstract. Any intelligent system performing evidence-based decision making under time pressure must negotiate a speed-accuracy trade-off. In computer science and engineering, this is typically modeled as minimizing a Bayes-risk functional that is a linear combination of expected decision delay and expected terminal decision loss. In neuroscience and psychology, however, it is often modeled as maximizing the long-term reward rate, or the ratio of expected terminal reward and expected decision delay. The two approaches have opposing advantages and disadvantages. While Bayes-risk minimization can be solved with powerful dynamic programming techniques unlike reward-rate maximization, it also requires the explicit specification of the relative costs of decision delay and error, which is obviated by reward-rate maximization. Here, we demonstrate that, for a large class of sequential multihypothesis identification problems under a stochastic deadline, the reward-rate maximization is equivalent to a special case of Bayes-risk minimization, in which the optimal policy that attains the minimal risk when the unit sampling cost is exactly the maximal reward rate is also the policy that attains maximal reward rate. We show that the maximum reward rate is the unique unit sampling cost for which the expected total observation cost and expected terminal reward break even under every Bayes-risk optimal decision rule. This interplay between reward-rate maximization and Bayesrisk minimization formulations allows us to show that maximum reward rate is always attained. We can compute the policy that maximizes reward rate by solving an inverse Bayes-risk minimization problem, whereby we know the Bayes risk of the optimal policy and need to find the associated unit sampling cost parameter. Leveraging this equivalence, we derive an iterative dynamic programming procedure for solving the reward-rate maximization problem exponentially fast, thus incorporating the advantages of both the reward-rate maximization and Bayes-risk minimization formulations. As an illustration, we will apply the procedure to a two-hypothesis identification example.

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“…Immediate cause explains why the event or occurrence has occurred. Remote cause may explain why the event or occurrence has occurred (Carbonell, 1979;Wagner et al, 2005;Hobbs, 2005;Bellinger, 2004;Mei, 2008;Ackoff, 1962;Wikipedia, 2009). …”

Section: Symptoms-immediate Cause-remote Causementioning

confidence: 99%

“…10. WOSSI map of simple relationships and attributes (Mei, 2008;Ackoff, 1962;Wikipedia, 2009;Wikipedia, 2009). …”

Section: Wossi Delivery Engine Of Cor Logic Gatesmentioning

confidence: 99%