Academic literature on the topic 'Information hypothesis'

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Journal articles on the topic "Information hypothesis"

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Rublík, František. "Testing a tolerance hypothesis by means of an information distance." Applications of Mathematics 35, no. 6 (1990): 458–70. http://dx.doi.org/10.21136/am.1990.104428.

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Díaz–Pachón, Daniel Andrés, Juan Pablo Sáenz, and J. Sunil Rao. "Hypothesis testing with active information." Statistics & Probability Letters 161 (June 2020): 108742. http://dx.doi.org/10.1016/j.spl.2020.108742.

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Jones, E. K., N. Denis, and D. Hunter. "Hypothesis management for information fusion." IEEE Aerospace and Electronic Systems Magazine 18, no. 6 (June 2003): 3–8. http://dx.doi.org/10.1109/maes.2003.1209583.

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Bailey, Kent R. "Borrowing information in hypothesis testing." Controlled Clinical Trials 9, no. 3 (September 1988): 251. http://dx.doi.org/10.1016/0197-2456(88)90096-7.

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Mock, Douglas W., Timothy C. Lamey, and Desmond B. A. Thompson. "Falsifiability and the Information Centre Hypothesis." Ornis Scandinavica 19, no. 3 (September 1988): 231. http://dx.doi.org/10.2307/3676564.

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Rybko, A., and S. Shlosman. "Poisson Hypothesis for Information Networks. I." Moscow Mathematical Journal 5, no. 3 (2005): 679–704. http://dx.doi.org/10.17323/1609-4514-2005-5-3-679-704.

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Rybko, A., and S. Shlosman. "Poisson Hypothesis for Information Networks. II." Moscow Mathematical Journal 5, no. 4 (2005): 927–59. http://dx.doi.org/10.17323/1609-4514-2005-5-4-927-959.

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Skov, Richard B., and Steven J. Sherman. "Information-gathering processes: Diagnosticity, hypothesis-confirmatory strategies, and perceived hypothesis confirmation." Journal of Experimental Social Psychology 22, no. 2 (March 1986): 93–121. http://dx.doi.org/10.1016/0022-1031(86)90031-4.

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Nagaoka, Hiroshi, and Masahito Hayashi. "An Information-Spectrum Approach to Classical and Quantum Hypothesis Testing for Simple Hypotheses." IEEE Transactions on Information Theory 53, no. 2 (February 2007): 534–49. http://dx.doi.org/10.1109/tit.2006.889463.

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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.
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Dissertations / Theses on the topic "Information hypothesis"

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Feeney, Aidan. "Information selection and belief updating in hypothesis evaluation." Thesis, University of Plymouth, 1996. http://hdl.handle.net/10026.1/344.

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This thesis is concerned with the factors underlying both selection and use of evidence in the testing of hypotheses. The work it describes examines the role played in hypothesis evaluation by background knowledge about the probability of events in the environment as well as the influence of more general constraints. Experiments on information choice showed that subjects were sensitive both to explicitly presented probabilistic information and to the likelihood of evidence with regard to background beliefs. It is argued - in contrast with other views in the literature - that subjects' choice of evidence to test hypotheses is rational allowing for certain constraints on subjects' cognitive representations. The majority of experiments in this thesis, however, are focused on the issue of how the information which subjects receive when testing hypotheses affects their beliefs. A major finding is that receipt of early information creates expectations which influence the response to later information. This typically produces a recency effect in which presenting strong evidence after weak evidence affects beliefs more than if the same evidence is presented in the opposite order. These findings run contrary to the view of the belief revision process which is prevalent in the literature in which it is generally assumed that the effects of successive pieces of information are independent. The experiments reported here also provide evidence that processes of selective attention influence evidence interpretation: subjects tend to focus on the most informative part of the evidence and may switch focus from one part of the evidence to another as the task progresses. in some cases, such changes of attention can eliminate the recency effect. In summary, the present research provides new evidence about the role of background beliefs, expectations and cognitive constraints in the selection and use of information to test hypotheses. Several new findings emerge which require revision to current accounts of information integration in the belief revision literature.
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O'Rourke, Sean Michael. "Information-theoretic and hypothesis-based clustering in bioinformatics." Diss., [La Jolla] : University of California, San Diego, 2009. http://wwwlib.umi.com/cr/ucsd/fullcit?p3356190.

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Thesis (Ph. D.)--University of California, San Diego, 2009.
Title from first page of PDF file (viewed July 7, 2009). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 98-105).
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Sullivan, Terry. "The Cluster Hypothesis: A Visual/Statistical Analysis." Thesis, University of North Texas, 2000. https://digital.library.unt.edu/ark:/67531/metadc2444/.

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By allowing judgments based on a small number of exemplar documents to be applied to a larger number of unexamined documents, clustered presentation of search results represents an intuitively attractive possibility for reducing the cognitive resource demands on human users of information retrieval systems. However, clustered presentation of search results is sensible only to the extent that naturally occurring similarity relationships among documents correspond to topically coherent clusters. The Cluster Hypothesis posits just such a systematic relationship between document similarity and topical relevance. To date, experimental validation of the Cluster Hypothesis has proved problematic, with collection-specific results both supporting and failing to support this fundamental theoretical postulate. The present study consists of two computational information visualization experiments, representing a two-tiered test of the Cluster Hypothesis under adverse conditions. Both experiments rely on multidimensionally scaled representations of interdocument similarity matrices. Experiment 1 is a term-reduction condition, in which descriptive titles are extracted from Associated Press news stories drawn from the TREC information retrieval test collection. The clustering behavior of these titles is compared to the behavior of the corresponding full text via statistical analysis of the visual characteristics of a two-dimensional similarity map. Experiment 2 is a dimensionality reduction condition, in which inter-item similarity coefficients for full text documents are scaled into a single dimension and then rendered as a two-dimensional visualization; the clustering behavior of relevant documents within these unidimensionally scaled representations is examined via visual and statistical methods. Taken as a whole, results of both experiments lend strong though not unqualified support to the Cluster Hypothesis. In Experiment 1, semantically meaningful 6.6-word document surrogates systematically conform to the predictions of the Cluster Hypothesis. In Experiment 2, the majority of the unidimensionally scaled datasets exhibit a marked nonuniformity of distribution of relevant documents, further supporting the Cluster Hypothesis. Results of the two experiments are profoundly question-specific. Post hoc analyses suggest that it may be possible to predict the success of clustered searching based on the lexical characteristics of users' natural-language expression of their information need.
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Lin, Chienting, Hsinchun Chen, and Jay F. Nunamaker. "Verifying the proximity and size hypothesis for self-organizing maps." M.E. Sharpe, Inc, 2000. http://hdl.handle.net/10150/106111.

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Artificial Intelligence Lab, Department of MIS, University of Arizona
The Kohonen Self-Organizing Map (SOM) is an unsupervised learning technique for summarizing high-dimensional data so that similar inputs are, in general, mapped close to one another. When applied to textual data, SOM has been shown to be able to group together related concepts in a data collection and to present major topics within the collection with larger regions. Research in which properties of SOM were validated, called the Proximity and Size Hypotheses,is presented through a user evaluation study. Building upon the previous research in automatic concept generation and classification, it is demonstrated that the Kohonen SOM was able to perform concept clustering effectively, based on its concept precision and recall7 scores as judged by human experts. A positive relationship between the size of an SOM region and the number of documents contained in the region is also demonstrated.
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Lehmann, Rüdiger, and Michael Lösler. "Multiple Outlier Detection: Hypothesis Tests versus Model Selection by Information Criteria." Hochschule für Technik und Wirtschaft Dresden, 2016. https://htw-dresden.qucosa.de/id/qucosa%3A23307.

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The detection of multiple outliers can be interpreted as a model selection problem. Models that can be selected are the null model, which indicates an outlier free set of observations, or a class of alternative models, which contain a set of additional bias parameters. A common way to select the right model is by using a statistical hypothesis test. In geodesy data snooping is most popular. Another approach arises from information theory. Here, the Akaike information criterion (AIC) is used to select an appropriate model for a given set of observations. The AIC is based on the Kullback-Leibler divergence, which describes the discrepancy between the model candidates. Both approaches are discussed and applied to test problems: the fitting of a straight line and a geodetic network. Some relationships between data snooping and information criteria are discussed. When compared, it turns out that the information criteria approach is more simple and elegant. Along with AIC there are many alternative information criteria for selecting different outliers, and it is not clear which one is optimal.
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Lehmann, Rüdiger, and Michael Lösler. "Multiple Outlier Detection: Hypothesis Tests versus Model Selection by Information Criteria." Hochschule für Technik und Wirtschaft Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:520-qucosa-225770.

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The detection of multiple outliers can be interpreted as a model selection problem. Models that can be selected are the null model, which indicates an outlier free set of observations, or a class of alternative models, which contain a set of additional bias parameters. A common way to select the right model is by using a statistical hypothesis test. In geodesy data snooping is most popular. Another approach arises from information theory. Here, the Akaike information criterion (AIC) is used to select an appropriate model for a given set of observations. The AIC is based on the Kullback-Leibler divergence, which describes the discrepancy between the model candidates. Both approaches are discussed and applied to test problems: the fitting of a straight line and a geodetic network. Some relationships between data snooping and information criteria are discussed. When compared, it turns out that the information criteria approach is more simple and elegant. Along with AIC there are many alternative information criteria for selecting different outliers, and it is not clear which one is optimal.
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Sechidis, Konstantinos. "Hypothesis testing and feature selection in semi-supervised data." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/hypothesis-testing-and-feature-selection-in-semisupervised-data(97f5f950-f020-4ace-b6cd-49cb2f88c730).html.

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A characteristic of most real world problems is that collecting unlabelled examples is easier and cheaper than collecting labelled ones. As a result, learning from partially labelled data is a crucial and demanding area of machine learning, and extending techniques from fully to partially supervised scenarios is a challenging problem. Our work focuses on two types of partially labelled data that can occur in binary problems: semi-supervised data, where the labelled set contains both positive and negative examples, and positive-unlabelled data, a more restricted version of partial supervision where the labelled set consists of only positive examples. In both settings, it is very important to explore a large number of features in order to derive useful and interpretable information about our classification task, and select a subset of features that contains most of the useful information. In this thesis, we address three fundamental and tightly coupled questions concerning feature selection in partially labelled data; all three relate to the highly controversial issue of when does additional unlabelled data improve performance in partially labelled learning environments and when does not. The first question is what are the properties of statistical hypothesis testing in such data? Second, given the widespread criticism of significance testing, what can we do in terms of effect size estimation, that is, quantification of how strong the dependency between feature X and the partially observed label Y? Finally, in the context of feature selection, how well can features be ranked by estimated measures, when the population values are unknown? The answers to these questions provide a comprehensive picture of feature selection in partially labelled data. Interesting applications include for estimation of mutual information quantities, structure learning in Bayesian networks, and investigation of how human-provided prior knowledge can overcome the restrictions of partial labelling. One direct contribution of our work is to enable valid statistical hypothesis testing and estimation in positive-unlabelled data. Focusing on a generalised likelihood ratio test and on estimating mutual information, we provide five key contributions. (1) We prove that assuming all unlabelled examples are negative cases is sufficient for independence testing, but not for power analysis activities. (2) We suggest a new methodology that compensates this and enables power analysis, allowing sample size determination for observing an effect with a desired power by incorporating user’s prior knowledge over the prevalence of positive examples. (3) We show a new capability, supervision determination, which can determine a-priori the number of labelled examples the user must collect before being able to observe a desired statistical effect. (4) We derive an estimator of the mutual information in positive-unlabelled data, and its asymptotic distribution. (5) Finally, we show how to rank features with and without prior knowledge. Also we derive extensions of these results to semi-supervised data. In another extension, we investigate how we can use our results for Markov blanket discovery in partially labelled data. While there are many different algorithms for deriving the Markov blanket of fully supervised nodes, the partially labelled problem is far more challenging, and there is a lack of principled approaches in the literature. Our work constitutes a generalization of the conditional tests of independence for partially labelled binary target variables, which can handle the two main partially labelled scenarios: positive-unlabelled and semi-supervised. The result is a significantly deeper understanding of how to control false negative errors in Markov Blanket discovery procedures and how unlabelled data can help. Finally, we present how our results can be used for information theoretic feature selection in partially labelled data. Our work extends naturally feature selection criteria suggested for fully-supervised data, to partially labelled scenarios. These criteria can capture both the relevancy and redundancy of the features and can be used for semi-supervised and positive-unlabelled data.
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Scheider, Linda [Verfasser]. "The command hypothesis versus the information hypothesis : how do domestic dogs (Canis familiaris) comprehend the human pointing gesture? / Linda Scheider." Berlin : Freie Universität Berlin, 2011. http://d-nb.info/1025939069/34.

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Bennett, Simon James. "Exploring the boundaries of the specificity of learning hypothesis." Thesis, Manchester Metropolitan University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320487.

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Yu, Angel On Kei. "The outcome of person-job fit: A test of the realistic information hypothesis." CSUSB ScholarWorks, 1995. https://scholarworks.lib.csusb.edu/etd-project/1232.

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Books on the topic "Information hypothesis"

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Machiels-Bongaerts, Maureen. Mobilizing prior knowledge in text processing: The selective-attention hypothesis versus the cognitive set-point hypothesis. [Maastricht]: Universitaire Pers Maastricht, 1993.

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Accelerating discovery: Mining unstructured information for hypothesis generation. Boca Raton: CRC Press, is an imprint of the Taylor & Francis Group, an Informa business, 2016.

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Bernheim, B. Douglas. A tax-based test of the dividend signaling hypothesis. Cambridge, MA: National Bureau of Economic Research, 1992.

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Haroutunian, Evgueni A. Reliability criteria in information theory and in statistical hypothesis testing. Hanover, MA: Now Publishers, 2008.

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Johnson, F. The uncertain information hypothesis: A test for the UK market. Dublin: University College Dublin, 1996.

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Bernhein, B. Douglas. A tax-based test of the dividend signaling hypothesis. Cambridge, Mass: National Bureau of Economic Research, 1992.

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Kognitive Dissonanz und Gewinn von Information als Konsequenzen eines kognitiven Hypothesentests. Pfaffenweiler: Centaurus-Verlagsgesellschaft, 1985.

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Fernandez-Corugedo, Emilio. Imperfect information and the aggregate stochastic implications of the life cycle hypothesis. Bristol: University of Bristol, Department of Economics, 1999.

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Butt, Jane L. The effects of information order and hypothesis-testing strategies on auditors' judgments. Lausanne: International Institute for Management Development, 1991.

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Connectionism, language production, and adult aphasia: Elaboration of a connectionist framework for lexical processing and a hypothesis of agrammatic aphasia. Helsinki: Societas Scientiarum Fennica, 1995.

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Book chapters on the topic "Information hypothesis"

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Castell, Lutz. "The Ur-Hypothesis." In Time, Quantum and Information, 363–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-10557-3_23.

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Krajíček, Jiří. "Information Hypothesis: On Human Information Capability Study." In Brain Informatics, 25–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04954-5_13.

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Eineborg, Martin. "Fuzzifying Hyperplanes in the Hypothesis Space." In Hybrid Information Systems, 313–22. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1782-9_23.

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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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Kurland, Oren. "The Cluster Hypothesis in Information Retrieval." In Lecture Notes in Computer Science, 823–26. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06028-6_105.

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Cogranne, Rémi, Cathel Zitzmann, Florent Retraint, Igor Nikiforov, Lionel Fillatre, and Philippe Cornu. "Statistical Detection of LSB Matching Using Hypothesis Testing Theory." In Information Hiding, 46–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36373-3_4.

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Hayashi, Masahito. "Quantum Hypothesis Testing and Discrimination of Quantum States." In Quantum Information Theory, 95–153. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49725-8_3.

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Nielsen, Frank. "Hypothesis Testing, Information Divergence and Computational Geometry." In Lecture Notes in Computer Science, 241–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40020-9_25.

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Ihler, Alexander T., John W. Fisher, and Alan S. Willsky. "Hypothesis Testing over Factorizations for Data Association." In Information Processing in Sensor Networks, 239–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36978-3_16.

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Conference papers on the topic "Information hypothesis"

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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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Wu, Lin, Fei Wang, Yongjun Xu, Yu Jiang, and Jiakai Wang. "A Parallel Implementation of Hypothesis-Oriented Multiple Hypothesis Tracking." In 2020 IEEE 23rd International Conference on Information Fusion (FUSION). IEEE, 2020. http://dx.doi.org/10.23919/fusion45008.2020.9190459.

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Kurland, Oren. "The cluster hypothesis in information retrieval." In SIGIR '13: The 36th International ACM SIGIR conference on research and development in Information Retrieval. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2484028.2484192.

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Li, Yun, Sirin Nitinawarat, and Venugopal V. Veeravalli. "Universal outlier hypothesis testing." In 2013 IEEE International Symposium on Information Theory (ISIT). IEEE, 2013. http://dx.doi.org/10.1109/isit.2013.6620710.

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Chang, Meng-Che, and Matthieu R. Bloch. "Evasive Active Hypothesis Testing." In 2020 IEEE International Symposium on Information Theory (ISIT). IEEE, 2020. http://dx.doi.org/10.1109/isit44484.2020.9174021.

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Pattanayak, Kunal, Vikram Krishnamurthy, and Erik Blasch. "Inverse Sequential Hypothesis Testing." In 2020 IEEE 23rd International Conference on Information Fusion (FUSION). IEEE, 2020. http://dx.doi.org/10.23919/fusion45008.2020.9190339.

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Salehkalaibar, Sadaf, Michele Wigger, and Ligong Wang. "Hypothesis testing over cascade channels." In 2017 IEEE Information Theory Workshop (ITW). IEEE, 2017. http://dx.doi.org/10.1109/itw.2017.8277994.

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Peng, Guanze, and Quanyan Zhu. "Sequential Hypothesis Testing Game." In 2020 54th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2020. http://dx.doi.org/10.1109/ciss48834.2020.1570617162.

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Naghshvar, Mohammad, and Tara Javidi. "Information utility in active sequential hypothesis testing." In 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2010. http://dx.doi.org/10.1109/allerton.2010.5706897.

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Roy, Jean. "Towards multiple hypothesis situation analysis." In 2007 10th International Conference on Information Fusion. IEEE, 2007. http://dx.doi.org/10.1109/icif.2007.4408032.

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Reports on the topic "Information hypothesis"

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Kitano, Hiroaki. Information Fusion for Hypothesis Generation under Uncertain and Partial Information Access Situation. Fort Belvoir, VA: Defense Technical Information Center, July 2006. http://dx.doi.org/10.21236/ada466198.

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Bonilla-Mejía, Leonardo, and Erika Londoño-Ortega. Geographic Isolation and Learning in Rural Schools. Banco de la República, August 2021. http://dx.doi.org/10.32468/be.1169.

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Rural schools are usually behind in terms of learning, and part of this could be related to geographical isolation. We explore this hypothesis, assessing the effect of distance between rural schools and local governments on learning in Colombia. We use spatial discontinuous regression models based on detailed administrative records from the education system and granular geographic information. Results indicate that distance to towns and Secretary of Education has significant negative effects on students’ standardized test scores. We evaluated alternative mechanisms, finding that the effect of distance is partly explained by differences in critical educational inputs, such as teachers’ education attainment and contract stability. Finally, we assess the mediating role of a program providing monetary incentives to teachers and principals in remote areas.
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Wickenden, Mary. Disabled Children and Work: An Overview of a Neglected Topic with a Specific Focus on Ghana. Institute of Development Studies (IDS), February 2021. http://dx.doi.org/10.19088/acha.2021.002.

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This paper provides an overview of issues related to disabled children and work. This is a very unexplored topic and the literature is scant, so the paper first provides an overview of some key relevant background information on: disability globally and in Ghana, disability and employment, disabled children and relevant human rights approaches – the UNCRC and UNCRPD. Next examples of research on disabled children and work are presented and lastly some suggested hypotheses and possible research questions are proposed.
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