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Artykuły w czasopismach na temat "Inference"
Barreyro, Juan Pablo, Jazmín Cevasco, Débora Burín i Carlos Molinari Marotto. "Working Memory Capacity and Individual Differences in the Making of Reinstatement and Elaborative Inferences". Spanish journal of psychology 15, nr 2 (lipiec 2012): 471–79. http://dx.doi.org/10.5209/rev_sjop.2012.v15.n2.38857.
Pełny tekst źródłaWang, Yingxu. "Inference Algebra (IA)". International Journal of Cognitive Informatics and Natural Intelligence 6, nr 1 (styczeń 2012): 21–47. http://dx.doi.org/10.4018/jcini.2012010102.
Pełny tekst źródłaWang, Yingxu. "Inference Algebra (IA)". International Journal of Cognitive Informatics and Natural Intelligence 5, nr 4 (październik 2011): 61–82. http://dx.doi.org/10.4018/jcini.2011100105.
Pełny tekst źródłaWilhelm, Marco, i Gabriele Kern-Isberner. "Focused Inference and System P". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 7 (18.05.2021): 6522–29. http://dx.doi.org/10.1609/aaai.v35i7.16808.
Pełny tekst źródłaStarns, Jeffrey J., Andrea M. Cataldo, Caren M. Rotello, Jeffrey Annis, Andrew Aschenbrenner, Arndt Bröder, Gregory Cox i in. "Assessing Theoretical Conclusions With Blinded Inference to Investigate a Potential Inference Crisis". Advances in Methods and Practices in Psychological Science 2, nr 4 (17.09.2019): 335–49. http://dx.doi.org/10.1177/2515245919869583.
Pełny tekst źródłaGeorge, Marie St, Suzanne Mannes i James E. Hoffman. "Individual Differences in Inference Generation: An ERP Analysis". Journal of Cognitive Neuroscience 9, nr 6 (listopad 1997): 776–87. http://dx.doi.org/10.1162/jocn.1997.9.6.776.
Pełny tekst źródłaMurza, Kimberly A., Chad Nye, Jamie B. Schwartz, Barbara J. Ehren i Debbie L. Hahs-Vaughn. "A Randomized Controlled Trial of an Inference Generation Strategy Intervention for Adults With High-Functioning Autism Spectrum Disorder". American Journal of Speech-Language Pathology 23, nr 3 (sierpień 2014): 461–73. http://dx.doi.org/10.1044/2014_ajslp-13-0012.
Pełny tekst źródłaBahri, Toufik, i Abdulqader A. Al Hussain. "Question Type and Order of Inference in Inferential Processes during Reading Comprehension". Perceptual and Motor Skills 85, nr 2 (październik 1997): 655–64. http://dx.doi.org/10.2466/pms.1997.85.2.655.
Pełny tekst źródłaBar-Haim, Roy, Ido Dagan i Jonathan Berant. "Knowledge-Based Textual Inference via Parse-Tree Transformations". Journal of Artificial Intelligence Research 54 (9.09.2015): 1–57. http://dx.doi.org/10.1613/jair.4584.
Pełny tekst źródłaLandis, Christopher B., i Joshua A. Kroll. "Mitigating Inference Risks with the NIST Privacy Framework". Proceedings on Privacy Enhancing Technologies 2024, nr 1 (styczeń 2024): 217–31. http://dx.doi.org/10.56553/popets-2024-0013.
Pełny tekst źródłaRozprawy doktorskie na temat "Inference"
Calabrese, Chris M. Eng Massachusetts Institute of Technology. "Distributed inference : combining variational inference with distributed computing". Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/85407.
Pełny tekst źródłaCataloged from PDF version of thesis.
Includes bibliographical references (pages 95-97).
The study of inference techniques and their use for solving complicated models has taken off in recent years, but as the models we attempt to solve become more complex, there is a worry that our inference techniques will be unable to produce results. Many problems are difficult to solve using current approaches because it takes too long for our implementations to converge on useful values. While coming up with more efficient inference algorithms may be the answer, we believe that an alternative approach to solving this complicated problem involves leveraging the computation power of multiple processors or machines with existing inference algorithms. This thesis describes the design and implementation of such a system by combining a variational inference implementation (Variational Message Passing) with a high-level distributed framework (Graphlab) and demonstrates that inference is performed faster on a few large graphical models when using this system.
by Chris Calabrese.
M. Eng.
Miller, J. Glenn (James). "Predictive inference". Diss., Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/24294.
Pełny tekst źródłaCleave, Nancy. "Ecological inference". Thesis, University of Liverpool, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.304826.
Pełny tekst źródłaHenke, Joseph D. "Visualizing inference". Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/91826.
Pełny tekst źródłaCataloged from PDF version of thesis.
Includes bibliographical references (pages 75-76).
Common Sense Inference is an increasingly attractive technique to make computer interfaces more in touch with how human users think. However, the results of the inference process are often hard to interpret and evaluate. Visualization has been successful in many other fields of science, but to date it has not been used much for visualizing the results of inference. This thesis presents Alar, an interface which allows dynamic exploration of the results of the inference process. It enables users to detect errors in the input data and fine tune how liberal or conservative the inference should be. It accomplishes this through novel extensions to the AnalogySpace framework for inference and visualizing concepts and even assertions as nodes in a graph, clustered by their semantic relatedness. A usability study was performed and the results show users were able to successfully use Alar to determine the cause of an incorrect inference.
by Joseph D. Henke.
M. Eng.
Zhai, Yongliang. "Stochastic processes, statistical inference and efficient algorithms for phylogenetic inference". Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/59095.
Pełny tekst źródłaScience, Faculty of
Statistics, Department of
Graduate
Wu, Jianrong. "Asymptotic likelihood inference". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq41050.pdf.
Pełny tekst źródłaMorris, Quaid Donald Jozef 1972. "Practical probabilistic inference". Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/29989.
Pełny tekst źródłaIncludes bibliographical references (leaves 157-163).
The design and use of expert systems for medical diagnosis remains an attractive goal. One such system, the Quick Medical Reference, Decision Theoretic (QMR-DT), is based on a Bayesian network. This very large-scale network models the appearance and manifestation of disease and has approximately 600 unobservable nodes and 4000 observable nodes that represent, respectively, the presence and measurable manifestation of disease in a patient. Exact inference of posterior distributions over the disease nodes is extremely intractable using generic algorithms. Inference can be made much more efficient by exploiting the QMR-DT's unique structure. Indeed, tailor-made inference algorithms for the QMR-DT efficiently generate exact disease posterior marginals for some diagnostic problems and accurate approximate posteriors for others. In this thesis, I identify a risk with using the QMR-DT disease posteriors for medical diagnosis. Specifically, I show that patients and physicians conspire to preferentially report findings that suggest the presence of disease. Because the QMR-DT does not contain an explicit model of this reporting bias, its disease posteriors may not be useful for diagnosis. Correcting these posteriors requires augmenting the QMR-DT with additional variables and dependencies that model the diagnostic procedure. I introduce the diagnostic QMR-DT (dQMR-DT), a Bayesian network containing both the QMR-DT and a simple model of the diagnostic procedure. Using diagnostic problems sampled from the dQMR-DT, I show the danger of doing diagnosis using disease posteriors from the unaugmented QMR-DT.
(cont.) I introduce a new class of approximate inference methods, based on feed-forward neural networks, for both the QMR-DT and the dQMR-DT. I show that these methods, recognition models, generate accurate approximate posteriors on the QMR-DT, on the dQMR-DT, and on a version of the dQMR-DT specified only indirectly through a set of presolved diagnostic problems.
by Quaid Donald Jozef Morris.
Ph.D.in Computational Neuroscience
Levine, Daniel S. Ph D. Massachusetts Institute of Technology. "Focused active inference". Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/95559.
Pełny tekst źródłaCataloged from PDF version of thesis.
Includes bibliographical references (pages 91-99).
In resource-constrained inferential settings, uncertainty can be efficiently minimized with respect to a resource budget by incorporating the most informative subset of observations - a problem known as active inference. Yet despite the myriad recent advances in both understanding and streamlining inference through probabilistic graphical models, which represent the structural sparsity of distributions, the propagation of information measures in these graphs is less well understood. Furthermore, active inference is an NP-hard problem, thus motivating investigation of bounds on the suboptimality of heuristic observation selectors. Prior work in active inference has considered only the unfocused problem, which assumes all latent states are of inferential interest. Often one learns a sparse, high-dimensional model from data and reuses that model for new queries that may arise. As any particular query involves only a subset of relevant latent states, this thesis explicitly considers the focused problem where irrelevant states are called nuisance variables. Marginalization of nuisances is potentially computationally expensive and induces a graph with less sparsity; observation selectors that treat nuisances as notionally relevant may fixate on reducing uncertainty in irrelevant dimensions. This thesis addresses two primary issues arising from the retention of nuisances in the problem and representing a gap in the existing observation selection literature. The interposition of nuisances between observations and relevant latent states necessitates the derivation of nonlocal information measures. This thesis presents propagation algorithms for nonlocal mutual information (MI) on universally embedded paths in Gaussian graphical models, as well as algorithms for estimating MI on Gaussian graphs with cycles via embedded substructures, engendering a significant computational improvement over existing linear algebraic methods. The presence of nuisances also undermines application of a technical diminishing returns condition called submodularity, which is typically used to bound the performance of greedy selection. This thesis introduces the concept of submodular relaxations, which can be used to generate online-computable performance bounds, and analyzes the class of optimal submodular relaxations providing the tightest such bounds.
by Daniel S. Levine.
Ph. D.
Olšarová, Nela. "Inference propojení komponent". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236505.
Pełny tekst źródłaMacCartney, Bill. "Natural language inference /". May be available electronically:, 2009. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.
Pełny tekst źródłaKsiążki na temat "Inference"
MCDONALD, STEPHANIE. INFERENCE. [Place of publication not identified]: RINGWOOD Publishing, 2019.
Znajdź pełny tekst źródłaOakes, Michael W. Statistical inference. Chestnut Hill, MA: Epidemiology Resources Inc., 1990.
Znajdź pełny tekst źródłaGarthwaite, Paul H. Statistical inference. Oxford: Oxford University Press, 2002.
Znajdź pełny tekst źródłaBazett, Trefor. Bayesian Inference. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95792-6.
Pełny tekst źródłaWieczorek, Wojciech. Grammatical Inference. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-46801-3.
Pełny tekst źródłaSchölkopf, Bernhard, Zhiyuan Luo i Vladimir Vovk, red. Empirical Inference. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41136-6.
Pełny tekst źródłaBromek, Tadeusz, i Elżbieta Pleszczyńska, red. Statistical Inference. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0575-7.
Pełny tekst źródłaHarney, Hanns Ludwig. Bayesian Inference. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41644-1.
Pełny tekst źródłaPanik, Michael J. Statistical Inference. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118309773.
Pełny tekst źródłaHonavar, Vasant, i Giora Slutzki, red. Grammatical Inference. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0054058.
Pełny tekst źródłaCzęści książek na temat "Inference"
Herkenhoff, Linda, i John Fogli. "Inference". W Applied Statistics for Business and Management using Microsoft Excel, 161–82. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8423-3_7.
Pełny tekst źródłaHooker, John N. "Inference". W Integrated Methods for Optimization, 223–369. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4614-1900-6_6.
Pełny tekst źródłaDobson, Annette J. "Inference". W An Introduction to Generalized Linear Models, 49–67. Boston, MA: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4899-7252-1_5.
Pełny tekst źródłaGooch, Jan W. "Inference". W Encyclopedic Dictionary of Polymers, 983. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15258.
Pełny tekst źródłaColetti, Giulianella, i Romano Scozzafava. "Inference". W Probabilistic Logic in a Coherent Setting, 137–61. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0474-9_16.
Pełny tekst źródłaGroppe, Sven. "Inference". W Data Management and Query Processing in Semantic Web Databases, 177–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19357-6_9.
Pełny tekst źródłaWeik, Martin H. "inference". W Computer Science and Communications Dictionary, 771. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_8893.
Pełny tekst źródłaWang, Yong. "Inference". W Encyclopedia of Systems Biology, 1019–20. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_368.
Pełny tekst źródłaHeumann, Christian, Michael Schomaker i Shalabh. "Inference". W Introduction to Statistics and Data Analysis, 181–208. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46162-5_9.
Pełny tekst źródłaRisby, Bonnie, i Robert K. Risby. "Inference". W Lollipop Logic, 61–71. Wyd. 2. New York: Routledge, 2023. http://dx.doi.org/10.4324/9781003387206-9.
Pełny tekst źródłaStreszczenia konferencji na temat "Inference"
Haldimann, Jonas, i Christoph Beierle. "Inference with System W Satisfies Syntax Splitting". W 19th International Conference on Principles of Knowledge Representation and Reasoning {KR-2022}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/kr.2022/41.
Pełny tekst źródłaKonieczny, Sébastien, Pierre Marquis i Srdjan Vesic. "Rational Inference Relations from Maximal Consistent Subsets Selection". W Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/242.
Pełny tekst źródłaSharma, Ashish, Puneesh Khanna i Jaimin Maniyar. "Screening Deep Learning Inference Accelerators at the Production Lines". W 9th International Conference on Foundations of Computer Science & Technology (CST 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121911.
Pełny tekst źródłaBorovcnik, Manfred. "Informal inference – approaches towards statistical inference". W Decision Making Based on Data. International Association for Statistical Education, 2019. http://dx.doi.org/10.52041/srap.19101.
Pełny tekst źródłaRamírez, Julio C. "Inference Optimization Approach in Fuzzy Inference Systems". W 2008 Electronics, Robotics and Automotive Mechanics Conference (CERMA). IEEE, 2008. http://dx.doi.org/10.1109/cerma.2008.42.
Pełny tekst źródłaNarra, Krishna Giri, Zhifeng Lin, Yongqin Wang, Keshav Balasubramanian i Murali Annavaram. "Origami Inference: Private Inference Using Hardware Enclaves". W 2021 IEEE 14th International Conference on Cloud Computing (CLOUD). IEEE, 2021. http://dx.doi.org/10.1109/cloud53861.2021.00021.
Pełny tekst źródłaCaticha, Ariel, Ali Mohammad-Djafari, Jean-François Bercher i Pierre Bessiére. "Entropic Inference". W BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: Proceedings of the 30th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2011. http://dx.doi.org/10.1063/1.3573619.
Pełny tekst źródłaAiken, Alexander, i David Gay. "Barrier inference". W the 25th ACM SIGPLAN-SIGACT symposium. New York, New York, USA: ACM Press, 1998. http://dx.doi.org/10.1145/268946.268974.
Pełny tekst źródłaFrank, Martin R., Piyawadee "Noi" Sukaviriya i James D. Foley. "Inference bear". W the conference. New York, New York, USA: ACM Press, 1995. http://dx.doi.org/10.1145/225434.225453.
Pełny tekst źródłaMu, Weiyan, i Xiaona Yuan. "Statistical inference for ANOVA under heteroscedasticity: Statistical inference". W 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet). IEEE, 2012. http://dx.doi.org/10.1109/cecnet.2012.6201745.
Pełny tekst źródłaRaporty organizacyjne na temat "Inference"
Kyburg Jr, Henry E. Probabilistic Inference and Non-Monotonic Inference. Fort Belvoir, VA: Defense Technical Information Center, styczeń 1989. http://dx.doi.org/10.21236/ada250603.
Pełny tekst źródłaKyburg Jr, Henry E. Probabilistic Inference. Fort Belvoir, VA: Defense Technical Information Center, styczeń 1992. http://dx.doi.org/10.21236/ada255471.
Pełny tekst źródłaGay, David. Barrier Inference. Fort Belvoir, VA: Defense Technical Information Center, lipiec 1997. http://dx.doi.org/10.21236/ada637072.
Pełny tekst źródłaWarde, Cardinal. Optical Inference Machines. Fort Belvoir, VA: Defense Technical Information Center, czerwiec 1988. http://dx.doi.org/10.21236/ada197880.
Pełny tekst źródłaChertkov, Michael, Sungsoo Ahn i Jinwoo Shin. Gauging Variational Inference. Office of Scientific and Technical Information (OSTI), maj 2017. http://dx.doi.org/10.2172/1360686.
Pełny tekst źródłaSmith, David E., Michael R. Genesereth i Matthew I. Ginsberg. Controlling Recursive Inference,. Fort Belvoir, VA: Defense Technical Information Center, czerwiec 1985. http://dx.doi.org/10.21236/ada327440.
Pełny tekst źródłaAndrews, Isaiah, Toru Kitagawa i Adam McCloskey. Inference on Winners. Cambridge, MA: National Bureau of Economic Research, styczeń 2019. http://dx.doi.org/10.3386/w25456.
Pełny tekst źródłaMcCloskey, Adam, Isaiah Andrews i Toru Kitagawa. Inference on winners. The IFS, maj 2018. http://dx.doi.org/10.1920/wp.cem.2018.3118.
Pełny tekst źródłaKitagawa, Toru, Isaiah Andrews i Adam McCloskey. Inference on winners. The IFS, styczeń 2019. http://dx.doi.org/10.1920/wp.cem.2018.7318.
Pełny tekst źródłaMetu, Somiya, i Adrienne Raglin. Inference Model Documentation. DEVCOM Army Research Laboratory, wrzesień 2023. http://dx.doi.org/10.21236/ad1210687.
Pełny tekst źródła