Letteratura scientifica selezionata sul tema "Distributed hypothesis testing"
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Articoli di riviste sul tema "Distributed hypothesis testing":
Sreekumar, Sreejith, Asaf Cohen e Deniz Gündüz. "Privacy-Aware Distributed Hypothesis Testing". Entropy 22, n. 6 (16 giugno 2020): 665. http://dx.doi.org/10.3390/e22060665.
Salehkalaibar, Sadaf, e Michèle Wigger. "Distributed Hypothesis Testing over Noisy Broadcast Channels". Information 12, n. 7 (29 giugno 2021): 268. http://dx.doi.org/10.3390/info12070268.
Chair, Z., e P. K. Varshney. "Distributed Bayesian hypothesis testing with distributed data fusion". IEEE Transactions on Systems, Man, and Cybernetics 18, n. 5 (1988): 695–99. http://dx.doi.org/10.1109/21.21597.
Gilani, Atefeh, Selma Belhadj Amor, Sadaf Salehkalaibar e Vincent Y. F. Tan. "Distributed Hypothesis Testing with Privacy Constraints". Entropy 21, n. 5 (7 maggio 2019): 478. http://dx.doi.org/10.3390/e21050478.
Pados, D., K. W. Halford, D. Kazakos e P. Papantoni-Kazakos. "Distributed binary hypothesis testing with feedback". IEEE Transactions on Systems, Man, and Cybernetics 25, n. 1 (1995): 21–42. http://dx.doi.org/10.1109/21.362967.
Lalitha, Anusha, Tara Javidi e Anand D. Sarwate. "Social Learning and Distributed Hypothesis Testing". IEEE Transactions on Information Theory 64, n. 9 (settembre 2018): 6161–79. http://dx.doi.org/10.1109/tit.2018.2837050.
Li, Zishuo, Yilin Mo e Fei Hao. "Distributed Sequential Hypothesis Testing With Byzantine Sensors". IEEE Transactions on Signal Processing 69 (2021): 3044–58. http://dx.doi.org/10.1109/tsp.2021.3075147.
Sreekumar, Sreejith, e Deniz Gunduz. "Distributed Hypothesis Testing Over Discrete Memoryless Channels". IEEE Transactions on Information Theory 66, n. 4 (aprile 2020): 2044–66. http://dx.doi.org/10.1109/tit.2019.2953750.
Escamilla, Pierre, Michele Wigger e Abdellatif Zaidi. "Distributed Hypothesis Testing: Cooperation and Concurrent Detection". IEEE Transactions on Information Theory 66, n. 12 (dicembre 2020): 7550–64. http://dx.doi.org/10.1109/tit.2020.3019654.
Li, Shang, e Xiaodong Wang. "Distributed Sequential Hypothesis Testing With Quantized Message-Exchange". IEEE Transactions on Information Theory 66, n. 1 (gennaio 2020): 350–67. http://dx.doi.org/10.1109/tit.2019.2947494.
Tesi sul tema "Distributed hypothesis testing":
Hamad, Mustapha. "Sharing resources for enhanced distributed hypothesis testing". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT029.
Distributed hypothesis testing has many applications in security, health monitoring, automotive car control, or anomaly detection. With the help of distributed sensors, the decision centers (DCs) in such systems aim to distinguish between a normal situation (null hypothesis) and an alert situation (alternative hypothesis). Our focus will be on maximizing the exponential decay of the type-II error probabilities (corresponding to missed detections), with increasing numbers of observations, while keeping the type-I error probabilities (corresponding to false alarms) below given thresholds. In this thesis, we assume that different systems or applications share the limited network resources and impose expected-rate constraints on the system's communication links. We characterize the first information-theoretic fundamental limits under expected-rate constraints for multi-sensor multi-DC systems. Our characterization reveals a new tradeoff between the maximum type-II error exponents at the different DCs that stems from different margins to exploit under expected-rate constraints corresponding to the DCs' different type-I error thresholds. We propose a new multiplexing and rate-sharing strategy to achieve the error-exponents. Our strategy also generalizes to any setup with expected-rate constraints with promising gains compared to the results on the same setup under maximum-rate constraints. The converse proof method that we use to characterize the information-theoretic limits can also be used to derive new strong converse results under maximum-rate constraints. It is even applicable to other problems such as distributed compression or computation
Wissinger, John W. (John Weakley). "Distributed nonparametric training algorithms for hypothesis testing networks". Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/12006.
Includes bibliographical references (p. 495-502).
by John W. Wissinger.
Ph.D.
Escamilla, Pierre. "On cooperative and concurrent detection in distributed hypothesis testing". Electronic Thesis or Diss., Institut polytechnique de Paris, 2019. http://www.theses.fr/2019IPPAT007.
Statistical inference plays a major role in the development of new technologies and inspires a large number of algorithms dedicated to detection, identification and estimation tasks. However, there is no theoretical guarantee for the performance of these algorithms. In this thesis we try to understand how sensors can best share their information in a network with communication constraints to detect the same or distinct events. We investigate different aspects of detector cooperation and how conflicting needs can best be met in the case of detection tasks. More specifically we study a hypothesis testing problem where each detector must maximize the decay exponent of the Type II error under a given Type I error constraint. As the detectors are interested in different information, a compromise between the achievable decay exponents of the Type II error appears. Our goal is to characterize the region of possible trade-offs between Type II error decay exponents. In massive sensor networks, the amount of information is often limited due to energy consumption and network saturation risks. We are therefore studying the case of the zero rate compression communication regime (i.e. the messages size increases sub-linearly with the number of observations). In this case we fully characterize the region of Type II error decay exponent. In configurations where the detectors have or do not have the same purposes. We also study the case of a network with positive compression rates (i.e. the messages size increases linearly with the number of observations). In this case we present subparts of the region of Type II error decay exponent. Finally, in the case of a single sensor single detector scenario with a positive compression rate, we propose a complete characterization of the optimal Type II error decay exponent for a family of Gaussian hypothesis testing problems
Atta-Asiamah, Ernest. "Distributed Inference for Degenerate U-Statistics with Application to One and Two Sample Test". Diss., North Dakota State University, 2020. https://hdl.handle.net/10365/31777.
Kang, Shin-jae. "Korea's export performance : three empirical essays". Diss., Manhattan, Kan. : Kansas State University, 2008. http://hdl.handle.net/2097/767.
Katz, Gil. "Détection binaire distribuée sous contraintes de communication". Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLC001/document.
In recents years, interest has been growing in research of different autonomous systems. From the self-dring car to the Internet of Things (IoT), it is clear that the ability of automated systems to make autonomous decisions in a timely manner is crucial in the 21st century. These systems will often operate under stricts constains over their resources. In this thesis, an information-theoric approach is taken to this problem, in hope that a fundamental understanding of the limitations and perspectives of such systems can help future engineers in designing them.Throughout this thesis, collaborative distributed binary decision problems are considered. Two statisticians are required to declare the correct probability measure of two jointly distributed memoryless process, denoted by $vct{X}^n=(X_1,dots,X_n)$ and $vct{Y}^n=(Y_1,dots,Y_n)$, out of two possible probability measures on finite alphabets, namely $P_{XY}$ and $P_{bar{X}bar{Y}}$. The marginal samples given by $vct{X}^n$ and $vct{Y}^n$ are assumed to be available at different locations.The statisticians are allowed to exchange limited amounts of data over a perfect channel with a maximum-rate constraint. Throughout the thesis, the nature of communication varies. First, only unidirectional communication is allowed. Using its own observations, the receiver of this communication is required to first identify the legitimacy of its sender by declaring the joint distribution of the process, and then depending on such authentication it generates an adequate reconstruction of the observations satisfying an average per-letter distortion. Bidirectional communication is subsequently considered, in a scenario that allows interactive communication between the participants
"A distributed hypothesis-testing team decision problem with communications cost". Laboratory for Information and Decision Systems, Massachusetts Institute of Technology], 1986. http://hdl.handle.net/1721.1/2919.
"On optimal distributed decision architectures in a hypothesis testing environment". Massachusetts Institute of Technology, Laboratory for Information and Decision Systems], 1990. http://hdl.handle.net/1721.1/3167.
Cover title.
Includes bibliographical references (p. 35-37).
Research supported by the National Science Foundation. NSF/IRI-8902755 Research supported by the Office of Naval Research. ONR/N00014-84-K-0519
Jithin, K. S. "Spectrum Sensing in Cognitive Radios using Distributed Sequential Detection". Thesis, 2013. http://etd.iisc.ac.in/handle/2005/3278.
Jithin, K. S. "Spectrum Sensing in Cognitive Radios using Distributed Sequential Detection". Thesis, 2013. http://hdl.handle.net/2005/3278.
Libri sul tema "Distributed hypothesis testing":
Gül, Gökhan. Robust and Distributed Hypothesis Testing. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49286-5.
Lemeshko, Boris, e Irina Veretel'nikova. Criteria for testing hypotheses about randomness and the absence of a trend. Application Guide. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1587437.
Gül, Gökhan. Robust and Distributed Hypothesis Testing. Springer International Publishing AG, 2017.
Gül, Gökhan. Robust and Distributed Hypothesis Testing. Springer, 2018.
Capitoli di libri sul tema "Distributed hypothesis testing":
Ahlswede, Rudolf. "Hypothesis Testing Under Communication Constraints". In Probabilistic Methods and Distributed Information, 509–32. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00312-8_22.
Varshney, Pramod K. "Information Theory and Distributed Hypothesis Testing". In Distributed Detection and Data Fusion, 233–50. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-1904-0_7.
PS, Chandrashekhara Thejaswi, e Ranjeet Kumar Patro. "Distributed Multiple Hypothesis Testing in Sensor Networks Under Bandwidth Constraint". In Distributed Computing and Internet Technology, 184–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11604655_22.
Papastavrou, Jason, e Michael Athans. "A Distributed Hypothesis-Testing Team Decision Problem with Communications Cost". In System Fault Diagnostics, Reliability and Related Knowledge-Based Approaches, 99–130. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3929-5_3.
Shan, Qihao, e Sanaz Mostaghim. "Collective Decision Making in Swarm Robotics with Distributed Bayesian Hypothesis Testing". In Lecture Notes in Computer Science, 55–67. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60376-2_5.
Carpenter, Gail A., e Stephen Grossberg. "Self-Organizing Cortical Networks for Distributed Hypothesis Testing and Recognition Learning". In Theory and Applications of Neural Networks, 3–27. London: Springer London, 1992. http://dx.doi.org/10.1007/978-1-4471-1833-6_1.
Lavigna, Anthony, Armand M. Makowski e John S. Baras. "A Continuous—Time Distributed Version of Wald’s Sequential Hypothesis Testing Problem". In Analysis and Optimization of Systems, 533–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/bfb0007587.
Wefelmeyer, Wolfgang. "Testing hypotheses on independent, not identically distributed models". In Mathematical Statistics and Probability Theory, 267–82. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3963-9_20.
Hibbert, D. Brynn, e J. Justin Gooding. "Hypothesis Testing". In Data Analysis for Chemistry. Oxford University Press, 2005. http://dx.doi.org/10.1093/oso/9780195162103.003.0008.
Bolognani, Saverio. "Grid Topology Identification via Distributed Statistical Hypothesis Testing". In Big Data Application in Power Systems, 281–301. Elsevier, 2018. http://dx.doi.org/10.1016/b978-0-12-811968-6.00013-9.
Atti di convegni sul tema "Distributed hypothesis testing":
Katz, Gil, Pablo Piantanida e Merouane Debbah. "Collaborative distributed hypothesis testing with general hypotheses". In 2016 IEEE International Symposium on Information Theory (ISIT). IEEE, 2016. http://dx.doi.org/10.1109/isit.2016.7541590.
Mhanna, Maggie, e Pablo Piantanida. "On secure distributed hypothesis testing". In 2015 IEEE International Symposium on Information Theory (ISIT). IEEE, 2015. http://dx.doi.org/10.1109/isit.2015.7282727.
Sreekumar, Sreejith, Deniz Gunduz e Asaf Cohen. "Distributed Hypothesis Testing Under Privacy Constraints". In 2018 IEEE Information Theory Workshop (ITW). IEEE, 2018. http://dx.doi.org/10.1109/itw.2018.8613433.
Lalitha, Anusha, Anand Sarwate e Tara Javidi. "Social learning and distributed hypothesis testing". In 2014 IEEE International Symposium on Information Theory (ISIT). IEEE, 2014. http://dx.doi.org/10.1109/isit.2014.6874893.
Sreekumar, Sreejith, e Deniz Gunduz. "Distributed hypothesis testing over noisy channels". In 2017 IEEE International Symposium on Information Theory (ISIT). IEEE, 2017. http://dx.doi.org/10.1109/isit.2017.8006675.
Escamilla, Pierre, Michele Wigger e Abdellatif Zaidi. "Distributed Hypothesis Testing with Concurrent Detections". In 2018 IEEE International Symposium on Information Theory (ISIT). IEEE, 2018. http://dx.doi.org/10.1109/isit.2018.8437906.
Escamilla, Pierre, Abdellatif Zaidi e Michele Wigger. "Distributed Hypothesis Testing with Collaborative Detection". In 2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2018. http://dx.doi.org/10.1109/allerton.2018.8635828.
Amor, Selma Belhadj, Atefeh Gilani, Sadaf Salehkalaibar e Vincent Y. F. Tan. "Distributed Hypothesis Testing with Privacy Constraints". In 2018 International Symposium on Information Theory and Its Applications (ISITA). IEEE, 2018. http://dx.doi.org/10.23919/isita.2018.8664261.
Rahman, Md Saifur, e Aaron B. Wagner. "Optimality of binning for distributed hypothesis testing". In 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2010. http://dx.doi.org/10.1109/allerton.2010.5706994.
Chair, Z., e P. Varshney. "Neyman-Pearson hypothesis testing in distributed networks". In 26th IEEE Conference on Decision and Control. IEEE, 1987. http://dx.doi.org/10.1109/cdc.1987.272807.
Rapporti di organizzazioni sul tema "Distributed hypothesis testing":
Chair, Zelneddine, e Pramod K. Varshney. On Hypothesis Testing in Distributed Sensor Networks. Fort Belvoir, VA: Defense Technical Information Center, novembre 1987. http://dx.doi.org/10.21236/ada195910.
LaVigna, Anthony, Armand M. Makowski e John S. Baras. A Continuous-Time Distributed Version of Wald's Sequential Hypothesis Testing Problem. Fort Belvoir, VA: Defense Technical Information Center, gennaio 1985. http://dx.doi.org/10.21236/ada453211.
Nuzman, Dwayne W. An Accumulate-Toward-the-Mode Approach to Confidence Intervals and Hypothesis Testing With Applications to Binomially Distributed Data. Fort Belvoir, VA: Defense Technical Information Center, febbraio 2010. http://dx.doi.org/10.21236/ada514638.