Active Hypothesis Testing: Beyond Chernoff-Stein

Kartik, Dhruva; Nayyar, Ashutosh; Mitra, Urbashi

doi:10.1109/isit.2019.8849436

Cited by 11 publications

(5 citation statements)

References 11 publications

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“…One way to study this problem is to introduce a 'discount factor' that exponentially suppresses rewards that are reached after many measurement rounds. One can then obtain upper and lower bounds on the minimal discounted measurement infidelity [28]. A potential line of future work is to extend these results to the case of non-trivial transition matrices.…”

Section: Discussionmentioning

confidence: 95%

Improving quantum state detection with adaptive sequential observations

Geller¹,

Cole²,

Glancy³

et al. 2022

Quantum Sci. Technol.

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For many quantum systems intended for information processing, one detects the logical state of a qubit by integrating a continuously observed quantity over time. For example, ion and atom qubits are typically measured by driving a cycling transition and counting the number of photons observed from the resulting fluorescence. Instead of recording only the total observed count in a fixed time interval, one can observe the photon arrival times and get a state detection advantage by using the temporal structure in a model such as a Hidden Markov Model. We study what further advantage may be achieved by applying pulses to adaptively transform the state during the observation. We give a three-state example where adaptively chosen transformations yield a clear advantage, and we compare performances on an ion example, where we see improvements in some regimes. We provide a software package that can be used for exploration of temporally resolved strategies with and without adaptively chosen transformations.

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Section: Discussionmentioning

confidence: 95%

Improving quantum state detection with adaptive sequential observations

Geller¹,

Cole²,

Glancy³

et al. 2022

Quantum Sci. Technol.

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show abstract

“…Alternatively, the decision-maker may choose from a subset of actions to control the information extracted from the observation data and enhance the decision-making fidelity [143], [144]. This approach, which is called active hypothesis testing, was first introduced by Chernoff [145] for the sequential case.…”

Section: A Abnormality Detection Problem Formulationmentioning

confidence: 99%

Abnormality Detection and Localization Schemes Using Molecular Communication Systems: A Survey

et al. 2023

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Abnormality detection and localization (ADL) have been studied widely in wireless sensor networks (WSNs) literature, where the sensors use electromagnetic waves for communication. Molecular communication (MC) has been introduced as an alternative approach for ADL in particular areas such as healthcare, being able to tackle the shortcomings of conventional WSNs, such as invasiveness, bioincompatibility, and high energy consumption. In this paper, we introduce a general framework for MCbased ADL, which consists of multiple tiers for sensing the abnormality and communication between different agents, including the sensors, the fusion center (FC), the gateway (GW), and the external node (e.g., a local cloud), and describe each tier and the agents in this framework. We classify and explain different abnormality recognition methods, the functional units of the sensors, and different sensor features. Further, we describe different types of interfaces required for converting the internal and external signals at the FC and GW. Moreover, we present a unified channel model for the sensing and communication links. We categorize the MC-based abnormality detection schemes based on the sensor mobility, cooperative detection, and cooperative sensing/activation. We also classify the localization approaches based on the sensor mobility and propulsion mechanisms and present a general framework for the externally-controllable localization systems. Finally, we present some challenges and future research directions to realize and develop MC-based systems for ADL. The important challenges in the MC-based systems lie in four main directions as implementation, system design, modeling, and methods, which need considerable attention from multidisciplinary perspectives.

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“…The mutual information belongs to the class of information utility functions [41]- [43]. Information utilities have been successfully employed in applications such as active sequential hypothesis testing [41], [42] and codes for communication channels with feedback [43]. In general, information utility functions employ a suitable measure of uncertainty and model the reduction of uncertainty at each stage.…”

Section: Rewards Beliefs and Equilibriamentioning

confidence: 99%

“…In general, information utility functions employ a suitable measure of uncertainty and model the reduction of uncertainty at each stage. Besides the reduction in entropy employed in this work, several other related uncertainty measures have been used such as the extrinsic Jensen-Shannon divergence [41], the average confidence level [42] and the expected reduction in the KL distance [43].…”

Section: Rewards Beliefs and Equilibriamentioning

confidence: 99%

Dynamic Information Sharing and Punishment Strategies

Ntemos¹,

Pikramenos²,

Kalouptsidis³

2021

Preprint

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In this paper we study the problem of information sharing among rational self-interested agents as a dynamic game of asymmetric information. We assume that the agents imperfectly observe a Markov chain and they are called to decide whether they will share their noisy observations or not at each time instant. We utilize the notion of conditional mutual information to evaluate the information being shared among the agents. The challenges that arise due to the interdependence of agents' information structure and decision-making are exhibited. For the finite horizon game we prove that agents do not have incentive to share information. In contrast, we show that cooperation can be sustained in the infinite horizon case by devising appropriate punishment strategies which are defined over the agents' beliefs on the system state. We show that these strategies are closed under the best-response mapping and that cooperation can be the optimal choice in some subsets of the state belief simplex. We characterize these equilibrium regions, prove uniqueness of a maximal equilibrium region and devise an algorithm for its approximate computation.

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Active Hypothesis Testing: Beyond Chernoff-Stein

Cited by 11 publications

References 11 publications

Improving quantum state detection with adaptive sequential observations

Improving quantum state detection with adaptive sequential observations

Abnormality Detection and Localization Schemes Using Molecular Communication Systems: A Survey

Dynamic Information Sharing and Punishment Strategies

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