Relevant bibliographies by topics / Distributed hypothesis testing

Academic literature on the topic 'Distributed hypothesis testing'

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Published: 1 June 2024

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Journal articles on the topic "Distributed hypothesis testing":

1

Sreekumar, Sreejith, Asaf Cohen, and Deniz Gündüz. "Privacy-Aware Distributed Hypothesis Testing." Entropy 22, no. 6 (June 16, 2020): 665. http://dx.doi.org/10.3390/e22060665.

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A distributed binary hypothesis testing (HT) problem involving two parties, a remote observer and a detector, is studied. The remote observer has access to a discrete memoryless source, and communicates its observations to the detector via a rate-limited noiseless channel. The detector observes another discrete memoryless source, and performs a binary hypothesis test on the joint distribution of its own observations with those of the observer. While the goal of the observer is to maximize the type II error exponent of the test for a given type I error probability constraint, it also wants to keep a private part of its observations as oblivious to the detector as possible. Considering both equivocation and average distortion under a causal disclosure assumption as possible measures of privacy, the trade-off between the communication rate from the observer to the detector, the type II error exponent, and privacy is studied. For the general HT problem, we establish single-letter inner bounds on both the rate-error exponent-equivocation and rate-error exponent-distortion trade-offs. Subsequently, single-letter characterizations for both trade-offs are obtained (i) for testing against conditional independence of the observer’s observations from those of the detector, given some additional side information at the detector; and (ii) when the communication rate constraint over the channel is zero. Finally, we show by providing a counter-example where the strong converse which holds for distributed HT without a privacy constraint does not hold when a privacy constraint is imposed. This implies that in general, the rate-error exponent-equivocation and rate-error exponent-distortion trade-offs are not independent of the type I error probability constraint.
2

Salehkalaibar, Sadaf, and Michèle Wigger. "Distributed Hypothesis Testing over Noisy Broadcast Channels." Information 12, no. 7 (June 29, 2021): 268. http://dx.doi.org/10.3390/info12070268.

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This paper studies binary hypothesis testing with a single sensor that communicates with two decision centers over a memoryless broadcast channel. The main focus lies on the tradeoff between the two type-II error exponents achievable at the two decision centers. In our proposed scheme, we can partially mitigate this tradeoff when the transmitter has a probability larger than 1/2 to distinguish the alternate hypotheses at the decision centers, i.e., the hypotheses under which the decision centers wish to maximize their error exponents. In the cases where these hypotheses cannot be distinguished at the transmitter (because both decision centers have the same alternative hypothesis or because the transmitter’s observations have the same marginal distribution under both hypotheses), our scheme shows an important tradeoff between the two exponents. The results in this paper thus reinforce the previous conclusions drawn for a setup where communication is over a common noiseless link. Compared to such a noiseless scenario, here, however, we observe that even when the transmitter can distinguish the two hypotheses, a small exponent tradeoff can persist, simply because the noise in the channel prevents the transmitter to perfectly describe its guess of the hypothesis to the two decision centers.
3

Chair, Z., and P. K. Varshney. "Distributed Bayesian hypothesis testing with distributed data fusion." IEEE Transactions on Systems, Man, and Cybernetics 18, no. 5 (1988): 695–99. http://dx.doi.org/10.1109/21.21597.

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Gilani, Atefeh, Selma Belhadj Amor, Sadaf Salehkalaibar, and Vincent Y. F. Tan. "Distributed Hypothesis Testing with Privacy Constraints." Entropy 21, no. 5 (May 7, 2019): 478. http://dx.doi.org/10.3390/e21050478.

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We revisit the distributed hypothesis testing (or hypothesis testing with communication constraints) problem from the viewpoint of privacy. Instead of observing the raw data directly, the transmitter observes a sanitized or randomized version of it. We impose an upper bound on the mutual information between the raw and randomized data. Under this scenario, the receiver, which is also provided with side information, is required to make a decision on whether the null or alternative hypothesis is in effect. We first provide a general lower bound on the type-II exponent for an arbitrary pair of hypotheses. Next, we show that if the distribution under the alternative hypothesis is the product of the marginals of the distribution under the null (i.e., testing against independence), then the exponent is known exactly. Moreover, we show that the strong converse property holds. Using ideas from Euclidean information theory, we also provide an approximate expression for the exponent when the communication rate is low and the privacy level is high. Finally, we illustrate our results with a binary and a Gaussian example.
5

Pados, D., K. W. Halford, D. Kazakos, and P. Papantoni-Kazakos. "Distributed binary hypothesis testing with feedback." IEEE Transactions on Systems, Man, and Cybernetics 25, no. 1 (1995): 21–42. http://dx.doi.org/10.1109/21.362967.

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Lalitha, Anusha, Tara Javidi, and Anand D. Sarwate. "Social Learning and Distributed Hypothesis Testing." IEEE Transactions on Information Theory 64, no. 9 (September 2018): 6161–79. http://dx.doi.org/10.1109/tit.2018.2837050.

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Li, Zishuo, Yilin Mo, and 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.

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8

Sreekumar, Sreejith, and Deniz Gunduz. "Distributed Hypothesis Testing Over Discrete Memoryless Channels." IEEE Transactions on Information Theory 66, no. 4 (April 2020): 2044–66. http://dx.doi.org/10.1109/tit.2019.2953750.

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Escamilla, Pierre, Michele Wigger, and Abdellatif Zaidi. "Distributed Hypothesis Testing: Cooperation and Concurrent Detection." IEEE Transactions on Information Theory 66, no. 12 (December 2020): 7550–64. http://dx.doi.org/10.1109/tit.2020.3019654.

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Li, Shang, and Xiaodong Wang. "Distributed Sequential Hypothesis Testing With Quantized Message-Exchange." IEEE Transactions on Information Theory 66, no. 1 (January 2020): 350–67. http://dx.doi.org/10.1109/tit.2019.2947494.

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Journal articles

Dissertations / Theses on the topic "Distributed hypothesis testing":

1

Hamad, Mustapha. "Sharing resources for enhanced distributed hypothesis testing." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT029.

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Les tests d'hypothèses distribués ont de nombreuses applications dans la sécurité, la surveillance de la santé, le contrôle automobile ou la détection d'anomalies. À l'aide de capteurs distribués, les centres de décision de ces systèmes visent à distinguer une situation normale (hypothèse nulle) d'une situation d'alerte (hypothèse alternative). Nous nous concentrons sur la maximisation de la décroissance exponentielle des probabilités d'erreur de type-II (correspondant aux détections manquées), avec un nombre croissant d'observations, tout en maintenant les probabilités d'erreur de type-I (correspondant aux fausses alertes) en dessous de seuils fixés. Dans cette thèse, nous supposons que différents systèmes ou applications partagent les ressources limitées du réseau et imposent des contraintes de taux moyen sur les liens de communication. Nous caractérisons les premières limites fondamentales de la théorie de l'information sous des contraintes de taux moyen pour les systèmes avec capteurs multiples et centres de décision multiples. Notre caractérisation révèle un nouveau compromis entre les exposants maximaux d'erreur de type-II aux différents centres de décision qui découle des différentes marges à exploiter sous des contraintes de taux moyen correspondant aux différents seuils d'erreur de type-I des centres de décision. Nous proposons une nouvelle stratégie de multiplexage et de partage du taux pour atteindre ces exposants d'erreur. Notre stratégie se généralise également à toute configuration avec des contraintes de taux moyen et permet d'obtenir des gains prometteurs par rapport aux résultats sur la même configuration avec des contraintes de taux maximal. La méthode de preuve de "converse" que nous utilisons pour caractériser ces limites théoriques peut également être utilisée pour dériver de nouveaux résultats de "converse forte" sous des contraintes de taux maximal. Elle est même applicable à d'autres problèmes tels que la compression ou le calcul distribué
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
2

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.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.
Includes bibliographical references (p. 495-502).
by John W. Wissinger.
Ph.D.
3

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.

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L’inférence statistique prend une place prépondérante dans le développement des nouvelles technologies et inspire un grand nombre d’algorithmes dédiés à des tâches de détection, d’identification et d’estimation. Cependant il n’existe pas de garantie théorique pour les performances de ces algorithmes. Dans cette thèse, nous considérons un réseau simplifié de capteurs communicant sous contraintes pour tenter de comprendre comment des détecteurs peuvent se partager au mieux les informations à leur disposition pour détecter un même événement ou des événements distincts. Nous investiguons différents aspects de la coopération entre détecteurs et comment des besoins contradictoires peuvent être satisfaits au mieux dans le cas de tâches de détection. Plus spécifiquement nous étudions un problème de test d’hypothèse où chaque détecteur doit maximiser l’exposant de décroissance de l’erreur de Type II sous une contrainte d’erreur de Type I donnée. Comme il y a plusieurs détecteurs intéressés par des informations distinctes, un compromis entre les vitesses de décroissance atteignables va apparaître. Notre but est de caractériser la région des compromis possibles entre exposants d’erreurs de Type II. Dans le cadre des réseaux de capteurs massifs, la quantité d’information est souvent soumise à des limitations pour des raisons de consommation d’énergie et de risques de saturation du réseau. Nous étudions donc, en particulier, le cas du régime de communication à taux de compression nul (i.e. le nombre de bits des messages croit de façon sous-linéaire avec le nombre d’observations). Dans ce cas, nous caractérisons complètement la région des exposants d’erreurs de Type II dans les configurations où les détecteurs peuvent avoir des buts différents. Nous étudierons aussi le cas d’un réseau avec des taux de compressions positifs (i.e. le nombre de bits des messages augmente de façon linéaire avec le nombre d’observations). Dans ce cas, nous présentons des sous-parties de la région des exposants d’erreur de Type II. Enfin, nous proposons dans le cas d’un problème point à point avec un taux de compression positif une caractérisation complète de l’exposant de l’erreur de Type II optimal pour une famille de tests gaussiens
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
4

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.

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In many hypothesis testing problems such as one-sample and two-sample test problems, the test statistics are degenerate U-statistics. One of the challenges in practice is the computation of U-statistics for a large sample size. Besides, for degenerate U-statistics, the limiting distribution is a mixture of weighted chi-squares, involving the eigenvalues of the kernel of the U-statistics. As a result, it’s not straightforward to construct the rejection region based on this asymptotic distribution. In this research, we aim to reduce the computation complexity of degenerate U-statistics and propose an easy-to-calibrate test statistic by using the divide-and-conquer method. Specifically, we randomly partition the full n data points into kn even disjoint groups, and compute U-statistics on each group and combine them by averaging to get a statistic Tn. We proved that the statistic Tn has the standard normal distribution as the limiting distribution. In this way, the running time is reduced from O(n^m) to O( n^m/km_n), where m is the order of the one sample U-statistics. Besides, for a given significance level , it’s easy to construct the rejection region. We apply our method to the goodness of fit test and two-sample test. The simulation and real data analysis show that the proposed test can achieve high power and fast running time for both one and two-sample tests.
5

Kang, Shin-jae. "Korea's export performance : three empirical essays." Diss., Manhattan, Kan. : Kansas State University, 2008. http://hdl.handle.net/2097/767.

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6

Katz, Gil. "Détection binaire distribuée sous contraintes de communication." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLC001/document.

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Ces dernières années, l'intérêt scientifique porté aux différents aspects des systèmes autonomes est en pleine croissance. Des voitures autonomes jusqu'à l'Internet des objets, il est clair que la capacité de systèmes à prendre des décision de manière autonome devient cruciale. De plus, ces systèmes opéreront avec des ressources limitées. Dans cette thèse, ces systèmes sont étudiés sous l'aspect de la théorie de l'information, dans l'espoir qu'une compréhension fondamentale de leurs limites et de leurs utilisations pourrait aider leur conception par les futures ingénieurs.Dans ce travail, divers problèmes de décision binaire distribuée et collaborative sont considérés. Deux participants doivent "déclarer" la mesure de probabilité de deux variables aléatoires, distribuées conjointement par un processus sans mémoire et désignées par $vct{X}^n=(X_1,dots,X_n)$ et $vct{Y}^n=(Y_1,dots,Y_n)$. Cette décision et prise entre deux mesures de probabilité possibles sur un alphabet fini, désignés $P_{XY}$ et $P_{bar{X}bar{Y}}$. Les prélèvements marginaux des variables aléatoires, $vct{X}^n$ et $vct{Y}^n$ sont supposés à être disponibles aux différents sites .Il est permis aux participants d'échanger des quantités limitées d'information sur un canal parfait avec un contraint de débit maximal. Durant cette thèse, la nature de cette communication varie. La communication unidirectionnelle est considérée d'abord, suivie par la considération de communication bidirectionnelle, qui permet des échanges interactifs entre les participants
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
7

"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.

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8

"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.

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Jason D. Papastavrou and Michael Athans.
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
9

Jithin, K. S. "Spectrum Sensing in Cognitive Radios using Distributed Sequential Detection." Thesis, 2013. http://etd.iisc.ac.in/handle/2005/3278.

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Cognitive Radios are emerging communication systems which efficiently utilize the unused licensed radio spectrum called spectral holes. They run Spectrum sensing algorithms to identify these spectral holes. These holes need to be identified at very low SNR (<=-20 dB) under multipath fading, unknown channel gains and noise power. Cooperative spectrum sensing which exploits spatial diversity has been found to be particularly effective in this rather daunting endeavor. However despite many recent studies, several open issues need to be addressed for such algorithms. In this thesis we provide some novel cooperative distributed algorithms and study their performance. We develop an energy efficient detector with low detection delay using decentralized sequential hypothesis testing. Our algorithm at the Cognitive Radios employ an asynchronous transmission scheme which takes into account the noise at the fusion center. We have developed a distributed algorithm, DualSPRT, in which Cognitive Radios (secondary users) sequentially collect the observations, make local decisions and send them to the fusion center. The fusion center sequentially processes these received local decisions corrupted by Gaussian noise to arrive at a final decision. Asymptotically, this algorithm is shown to achieve the performance of the optimal centralized test, which does not consider fusion center noise. We also theoretically analyze its probability of error and average detection delay. Even though DualSPRT performs asymptotically well, a modification at the fusion node provides more control over the design of the algorithm parameters which then performs better at the usual operating probabilities of error in Cognitive Radio systems. We also analyze the modified algorithm theoretically. DualSPRT requires full knowledge of channel gains. Thus we extend the algorithm to take care the imperfections in channel gain estimates. We also consider the case when the knowledge about the noise power and channel gain statistic is not available at the Cognitive Radios. This problem is framed as a universal sequential hypothesis testing problem. We use easily implementable universal lossless source codes to propose simple algorithms for such a setup. Asymptotic performance of the algorithm is presented. A cooperative algorithm is also designed for such a scenario. Finally, decentralized multihypothesis sequential tests, which are relevant when the interest is to detect not only the presence of primary users but also their identity among multiple primary users, are also considered. Using the insight gained from binary hypothesis case, two new algorithms are proposed.
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Jithin, K. S. "Spectrum Sensing in Cognitive Radios using Distributed Sequential Detection." Thesis, 2013. http://hdl.handle.net/2005/3278.

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Cognitive Radios are emerging communication systems which efficiently utilize the unused licensed radio spectrum called spectral holes. They run Spectrum sensing algorithms to identify these spectral holes. These holes need to be identified at very low SNR (<=-20 dB) under multipath fading, unknown channel gains and noise power. Cooperative spectrum sensing which exploits spatial diversity has been found to be particularly effective in this rather daunting endeavor. However despite many recent studies, several open issues need to be addressed for such algorithms. In this thesis we provide some novel cooperative distributed algorithms and study their performance. We develop an energy efficient detector with low detection delay using decentralized sequential hypothesis testing. Our algorithm at the Cognitive Radios employ an asynchronous transmission scheme which takes into account the noise at the fusion center. We have developed a distributed algorithm, DualSPRT, in which Cognitive Radios (secondary users) sequentially collect the observations, make local decisions and send them to the fusion center. The fusion center sequentially processes these received local decisions corrupted by Gaussian noise to arrive at a final decision. Asymptotically, this algorithm is shown to achieve the performance of the optimal centralized test, which does not consider fusion center noise. We also theoretically analyze its probability of error and average detection delay. Even though DualSPRT performs asymptotically well, a modification at the fusion node provides more control over the design of the algorithm parameters which then performs better at the usual operating probabilities of error in Cognitive Radio systems. We also analyze the modified algorithm theoretically. DualSPRT requires full knowledge of channel gains. Thus we extend the algorithm to take care the imperfections in channel gain estimates. We also consider the case when the knowledge about the noise power and channel gain statistic is not available at the Cognitive Radios. This problem is framed as a universal sequential hypothesis testing problem. We use easily implementable universal lossless source codes to propose simple algorithms for such a setup. Asymptotic performance of the algorithm is presented. A cooperative algorithm is also designed for such a scenario. Finally, decentralized multihypothesis sequential tests, which are relevant when the interest is to detect not only the presence of primary users but also their identity among multiple primary users, are also considered. Using the insight gained from binary hypothesis case, two new algorithms are proposed.

Books on the topic "Distributed hypothesis testing":

1

Gül, Gökhan. Robust and Distributed Hypothesis Testing. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49286-5.

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Lemeshko, Boris, and 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.

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The monograph discusses the application of statistical criteria aimed at testing hypotheses about the absence of a trend in the analyzed samples. The rejection of such a hypothesis gives grounds to consider the analyzed data as samples of independent equally distributed random variables. We consider a set of special criteria aimed at testing such hypotheses, as well as a set of criteria for the uniformity of laws, the uniformity of averages and the uniformity of variances, which can also be used for these purposes. The disadvantages and advantages of various criteria are emphasized, the application of criteria in conditions of violation of standard assumptions is considered. Estimates of the power of the criteria are given, which allows you to navigate when choosing the most preferred criteria. Following the recommendations will ensure the correctness and increase the validity of statistical conclusions when analyzing data. It is intended for specialists who are interested in the application of statistical methods for the analysis of various aspects and trends of the surrounding reality and who are in contact with the processing of experimental results, the need for data analysis in their activities. It will be useful for engineers, researchers, specialists of various profiles (doctors, biologists, sociologists, economists, etc.) who face the need for statistical analysis of experimental results in their activities. It will also be useful for university teachers, graduate students and students.
3

Gül, Gökhan. Robust and Distributed Hypothesis Testing. Springer International Publishing AG, 2017.

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Gül, Gökhan. Robust and Distributed Hypothesis Testing. Springer, 2018.

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Book chapters on the topic "Distributed hypothesis testing":

1

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.

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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.

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PS, Chandrashekhara Thejaswi, and 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.

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Papastavrou, Jason, and 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.

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Shan, Qihao, and 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.

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Carpenter, Gail A., and 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.

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Lavigna, Anthony, Armand M. Makowski, and 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.

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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.

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Hibbert, D. Brynn, and J. Justin Gooding. "Hypothesis Testing." In Data Analysis for Chemistry. Oxford University Press, 2005. http://dx.doi.org/10.1093/oso/9780195162103.003.0008.

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Abstract:
• To understand the concept of the null hypothesis and the role of Type I and Type II errors. • To test that data are normally distributed and whether a datum is an outlier. • To determine whether there is systematic error in the mean of measurement results. • To perform tests to compare the means of two sets of data.… One of the uses to which data analysis is put is to answer questions about the data, or about the system that the data describes. In the former category are ‘‘is the data normally distributed?’’ and ‘‘are there any outliers in the data?’’ (see the discussions in chapter 1). Questions about the system might be ‘‘is the level of alcohol in the suspect’s blood greater than 0.05 g/100 mL?’’ or ‘‘does the new sensor give the same results as the traditional method?’’ In answering these questions we determine the probability of finding the data given the truth of a stated hypothesis—hence ‘‘hypothesis testing.’’ A hypothesis is a statement that might, or might not, be true. Usually the hypothesis is set up in such a way that it is possible to calculate the probability (P) of the data (or the test statistic calculated from the data) given the hypothesis, and then to make a decision about whether the hypothesis is to be accepted (high P) or rejected (low P). A particular case of a hypothesis test is one that determines whether or not the difference between two values is significant—a significance test. For this case we actually put forward the hypothesis that there is no real difference and the observed difference arises from random effects: it is called the null hypothesis (H<sub>0</sub>). If the probability that the data are consistent with the null hypothesis falls below a predetermined low value (say 0.05 or 0.01), then the hypothesis is rejected at that probability. Therefore, p<0.05 means that if the null hypothesis were true we would find the observed data (or more accurately the value of the statistic, or greater, calculated from the data) in less than 5% of repeated experiments.
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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.

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Conference papers on the topic "Distributed hypothesis testing":

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Katz, Gil, Pablo Piantanida, and 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.

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Mhanna, Maggie, and 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.

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Sreekumar, Sreejith, Deniz Gunduz, and 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.

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Lalitha, Anusha, Anand Sarwate, and 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.

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Sreekumar, Sreejith, and 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.

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Escamilla, Pierre, Michele Wigger, and 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.

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Escamilla, Pierre, Abdellatif Zaidi, and 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.

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Amor, Selma Belhadj, Atefeh Gilani, Sadaf Salehkalaibar, and 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.

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Rahman, Md Saifur, and 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.

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Chair, Z., and 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.

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Reports on the topic "Distributed hypothesis testing":

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Chair, Zelneddine, and Pramod K. Varshney. On Hypothesis Testing in Distributed Sensor Networks. Fort Belvoir, VA: Defense Technical Information Center, November 1987. http://dx.doi.org/10.21236/ada195910.

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LaVigna, Anthony, Armand M. Makowski, and John S. Baras. A Continuous-Time Distributed Version of Wald's Sequential Hypothesis Testing Problem. Fort Belvoir, VA: Defense Technical Information Center, January 1985. http://dx.doi.org/10.21236/ada453211.

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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, February 2010. http://dx.doi.org/10.21236/ada514638.

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