Relevant bibliographies by topics / Inference

Contents

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

Academic literature on the topic 'Inference'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Inference"

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Barreyro, Juan Pablo, Jazmín Cevasco, Débora Burín, and Carlos Molinari Marotto. "Working Memory Capacity and Individual Differences in the Making of Reinstatement and Elaborative Inferences." Spanish journal of psychology 15, no. 2 (2012): 471–79. http://dx.doi.org/10.5209/rev_sjop.2012.v15.n2.38857.

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This study investigated the role of working memory capacity on the making of reinstatement and causal elaborative inferences during the reading of natural texts. In order to determine participants' working memory capacity, they were asked to take the reading span task before they took part in the study. Those participants that were identified as high or low working memory capacity readers were asked to perform a lexical decision task in two conditions: pre-inference and inference. In the pre-inference condition, target words representing reinstatement or causal elaborative inferences were pres
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Wang, Yingxu. "Inference Algebra (IA)." International Journal of Cognitive Informatics and Natural Intelligence 6, no. 1 (2012): 21–47. http://dx.doi.org/10.4018/jcini.2012010102.

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Inference as the basic mechanism of thought is abilities gifted to human beings, which is a cognitive process that creates rational causations between a pair of cause and effect based on empirical arguments, formal reasoning, and/or statistical norms. It’s recognized that a coherent theory and mathematical means are needed for dealing with formal causal inferences. Presented is a novel denotational mathematical means for formal inferences known as Inference Algebra (IA) and structured as a set of algebraic operators on a set of formal causations. The taxonomy and framework of formal causal inf
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Wang, Yingxu. "Inference Algebra (IA)." International Journal of Cognitive Informatics and Natural Intelligence 5, no. 4 (2011): 61–82. http://dx.doi.org/10.4018/jcini.2011100105.

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Inference as the basic mechanism of thought is one of the gifted abilities of human beings. It is recognized that a coherent theory and mathematical means are needed for dealing with formal causal inferences. This paper presents a novel denotational mathematical means for formal inferences known as Inference Algebra (IA). IA is structured as a set of algebraic operators on a set of formal causations. The taxonomy and framework of formal causal inferences of IA are explored in three categories: a) Logical inferences on Boolean, fuzzy, and general logic causations; b) Analytic inferences on gene
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Wilhelm, Marco, and Gabriele Kern-Isberner. "Focused Inference and System P." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 7 (2021): 6522–29. http://dx.doi.org/10.1609/aaai.v35i7.16808.

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We bring in the concept of focused inference into the field of qualitative nonmonotonic reasoning by applying focused inference to System P. The idea behind drawing focused inferences is to concentrate on knowledge which seems to be relevant for answering a query while completely disregarding the remaining knowledge even at the risk of missing some meaningful information. Focused inference is motivated by mimicking snap decisions of human reasoners and aims on rapidly drawing still reasonable inferences from large sets of knowledge. In this paper, we define a series of query-dependent, syntact
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George, Marie St, Suzanne Mannes, and James E. Hoffman. "Individual Differences in Inference Generation: An ERP Analysis." Journal of Cognitive Neuroscience 9, no. 6 (1997): 776–87. http://dx.doi.org/10.1162/jocn.1997.9.6.776.

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Readers routinely draw inferences with remarkable efficiency and seemingly little cognitive effort. The present study was designed to explore different types of inferences during the course of reading, and the potential effects of differing levels of working memory capacity on the likelihood that inferences would be made. The electroencephalogram (EEG) was recorded from five scalp sites while participants read 90 paragraphs, composed of 60 experimental paragraphs and 30 filler paragraphs. Each experimental paragraph was four sentences long, and the final sentence stated explicitly the inferenc
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Murza, Kimberly A., Chad Nye, Jamie B. Schwartz, Barbara J. Ehren, and 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, no. 3 (2014): 461–73. http://dx.doi.org/10.1044/2014_ajslp-13-0012.

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PurposeThe present intervention study investigated the efficacy of the ACT & Check Strategy intervention to improve inference generation when reading, metacognitive ability, general reading comprehension, and social inference ability in adults with high-functioning autism spectrum disorder (HF-ASD).MethodTwenty-five adults with HF-ASD were randomly assigned to either a treatment or a control group. Treatment sessions were conducted in 1-hr sessions, twice a week, for a total of 6 weeks. Treatment focused on explicit instruction of components of inference generation, categories of inference
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Starns, Jeffrey J., Andrea M. Cataldo, Caren M. Rotello, et al. "Assessing Theoretical Conclusions With Blinded Inference to Investigate a Potential Inference Crisis." Advances in Methods and Practices in Psychological Science 2, no. 4 (2019): 335–49. http://dx.doi.org/10.1177/2515245919869583.

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Scientific advances across a range of disciplines hinge on the ability to make inferences about unobservable theoretical entities on the basis of empirical data patterns. Accurate inferences rely on both discovering valid, replicable data patterns and accurately interpreting those patterns in terms of their implications for theoretical constructs. The replication crisis in science has led to widespread efforts to improve the reliability of research findings, but comparatively little attention has been devoted to the validity of inferences based on those findings. Using an example from cognitiv
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Krishna Raju, Rama. "Dynamic Memory Inference Network for Natural Language Inference (2017)." International Journal of Science and Research (IJSR) 6, no. 2 (2017): 2219–30. http://dx.doi.org/10.21275/sr24926091431.

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Bahri, Toufik, and Abdulqader A. Al Hussain. "Question Type and Order of Inference in Inferential Processes during Reading Comprehension." Perceptual and Motor Skills 85, no. 2 (1997): 655–64. http://dx.doi.org/10.2466/pms.1997.85.2.655.

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For three groups of 20 subjects each who participated reading time was examined when stories suggested goal and state inferences which could be made by readers when asked state questions, goal questions, or no questions at all. Order of inference statement was also used as a variable. In addition, inferable statements were either left in or out of the text. Subjects read an equal number (12) of stories. Analysis showed that state inference took longer time than goal inference. Also, it took longer for subjects to draw inferences when the inferrable statement was absent than when it was present
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Bar-Haim, Roy, Ido Dagan, and Jonathan Berant. "Knowledge-Based Textual Inference via Parse-Tree Transformations." Journal of Artificial Intelligence Research 54 (September 9, 2015): 1–57. http://dx.doi.org/10.1613/jair.4584.

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Textual inference is an important component in many applications for understanding natural language. Classical approaches to textual inference rely on logical representations for meaning, which may be regarded as "external" to the natural language itself. However, practical applications usually adopt shallower lexical or lexical-syntactic representations, which correspond closely to language structure. In many cases, such approaches lack a principled meaning representation and inference framework. We describe an inference formalism that operates directly on language-based structures, particula
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Dissertations / Theses on the topic "Inference"

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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.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 95-97).<br>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 use
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Miller, J. Glenn (James). "Predictive inference." Diss., Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/24294.

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Cleave, Nancy. "Ecological inference." Thesis, University of Liverpool, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.304826.

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Henke, Joseph D. "Visualizing inference." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/91826.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 75-76).<br>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 p
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Zhai, Yongliang. "Stochastic processes, statistical inference and efficient algorithms for phylogenetic inference." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/59095.

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Phylogenetic inference aims to reconstruct the evolutionary history of populations or species. With the rapid expansion of genetic data available, statistical methods play an increasingly important role in phylogenetic inference by analyzing genetic variation of observed data collected at current populations or species. In this thesis, we develop new evolutionary models, statistical inference methods and efficient algorithms for reconstructing phylogenetic trees at the level of populations using single nucleotide polymorphism data and at the level of species using multiple sequence alignment d
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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.

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Morris, Quaid Donald Jozef 1972. "Practical probabilistic inference." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/29989.

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Thesis (Ph. D. in Computational Neuroscience)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2003.<br>Includes bibliographical references (leaves 157-163).<br>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 manif
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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.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 91-99).<br>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 distr
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Olšarová, Nela. "Inference propojení komponent." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236505.

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The Master Thesis deals with the design of hardware component interconnection inference algorithm that is supposed to be used in the FPGA schema editor that was integrated into educational integrated development environment VLAM IDE. The aim of the algorithm is to support user by finding an optimal interconnection of two given components. The editor and the development environment are implemented as an Eclipse plugin using GMF framework. A brief description of this technologies and the embedded systems design are followed by the design of the inference algorithm. This problem is a topic of com
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MacCartney, Bill. "Natural language inference /." May be available electronically:, 2009. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.

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Books on the topic "Inference"

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Bazett, Trefor. Bayesian Inference. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95792-6.

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Wieczorek, Wojciech. Grammatical Inference. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-46801-3.

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Schölkopf, Bernhard, Zhiyuan Luo, and Vladimir Vovk, eds. Empirical Inference. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41136-6.

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Bromek, Tadeusz, and Elżbieta Pleszczyńska, eds. Statistical Inference. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0575-7.

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Harney, Hanns Ludwig. Bayesian Inference. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41644-1.

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Panik, Michael J. Statistical Inference. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118309773.

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Honavar, Vasant, and Giora Slutzki, eds. Grammatical Inference. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0054058.

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Pouly, Marc, and Jürg Kohlas. Generic Inference. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118010877.

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Harney, Hanns L. Bayesian Inference. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-06006-3.

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Verbelen, Tim, Pablo Lanillos, Christopher L. Buckley, and Cedric De Boom, eds. Active Inference. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64919-7.

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Book chapters on the topic "Inference"

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Herkenhoff, Linda, and John Fogli. "Inference." In Applied Statistics for Business and Management using Microsoft Excel. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8423-3_7.

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Hooker, John N. "Inference." In Integrated Methods for Optimization. Springer US, 2011. http://dx.doi.org/10.1007/978-1-4614-1900-6_6.

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Dobson, Annette J. "Inference." In An Introduction to Generalized Linear Models. Springer US, 1990. http://dx.doi.org/10.1007/978-1-4899-7252-1_5.

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Gooch, Jan W. "Inference." In Encyclopedic Dictionary of Polymers. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15258.

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Coletti, Giulianella, and Romano Scozzafava. "Inference." In Probabilistic Logic in a Coherent Setting. Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0474-9_16.

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Groppe, Sven. "Inference." In Data Management and Query Processing in Semantic Web Databases. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19357-6_9.

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Weik, Martin H. "inference." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_8893.

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Wang, Yong. "Inference." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_368.

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Heumann, Christian, Michael Schomaker, and Shalabh. "Inference." In Introduction to Statistics and Data Analysis. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46162-5_9.

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Risby, Bonnie, and Robert K. Risby. "Inference." In Lollipop Logic, 2nd ed. Routledge, 2023. http://dx.doi.org/10.4324/9781003387206-9.

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Conference papers on the topic "Inference"

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Aifer, Maxwell, Samuel Duffield, Kaelan Donatella, et al. "Thermodynamic Bayesian Inference." In 2024 IEEE International Conference on Rebooting Computing (ICRC). IEEE, 2024. https://doi.org/10.1109/icrc64395.2024.10937023.

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Haldimann, Jonas, and Christoph Beierle. "Inference with System W Satisfies Syntax Splitting." In 19th International Conference on Principles of Knowledge Representation and Reasoning {KR-2022}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/kr.2022/41.

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In this paper, we investigate inductive inference with system W from conditional belief bases with respect to syntax splitting. The concept of syntax splitting for inductive inference states that inferences about independent parts of the signature should not affect each other. This was captured in work by Kern-Isberner, Beierle, and Brewka in the form of postulates for inductive inference operators expressing syntax splitting as a combination of relevance and independence; it was also shown that c-inference fulfils syntax splitting, while system P inference and system Z both fail to satisfy it
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Konieczny, Sébastien, Pierre Marquis, and Srdjan Vesic. "Rational Inference Relations from Maximal Consistent Subsets Selection." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/242.

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When one wants to draw non-trivial inferences from an inconsistent belief base, a very natural approach is to take advantage of the maximal consistent subsets of the base. But few inference relations from maximal consistent subsets exist. In this paper we point out new such relations based on selection of some of the maximal consistent subsets, leading thus to inference relations with a stronger inferential power. The selection process must obey some principles to ensure that it leads to an inference relation which is rational. We define a general class of monotonic selection relations for com
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Sharma, Ashish, Puneesh Khanna, and Jaimin Maniyar. "Screening Deep Learning Inference Accelerators at the Production Lines." In 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.

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Artificial Intelligence (AI) accelerators can be divided into two main buckets, one for training and another for inference over the trained models. Computation results of AI inference chipsets are expected to be deterministic for a given input. There are different compute engines on the Inference chip which help in acceleration of the Arithmetic operations. The Inference output results are compared with a golden reference output for the accuracy measurements. There can be many errors which can occur during the Inference execution. These errors could be due to the faulty hardware units and thes
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Borovcnik, Manfred. "Informal inference – approaches towards statistical inference." In Decision Making Based on Data. International Association for Statistical Education, 2019. http://dx.doi.org/10.52041/srap.19101.

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The development of methods suitable to tackle the problem of inductive logic – how to justify arguments that generalise findings from data – has been signified by great controversies in the foundations and – later – also in statistics education. There have been several attempts to reconcile the various approaches or to simplify statistical inference: EDA, Non-parametric statistics, and the Bootstrap. EDA focuses on a strong connection between data and context, non parametrics reduces the complexity of the model, and Bootstrap rests solely on the data. Informal inference subsumes two different
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Ramírez, Julio C. "Inference Optimization Approach in Fuzzy Inference Systems." In 2008 Electronics, Robotics and Automotive Mechanics Conference (CERMA). IEEE, 2008. http://dx.doi.org/10.1109/cerma.2008.42.

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Narra, Krishna Giri, Zhifeng Lin, Yongqin Wang, Keshav Balasubramanian, and Murali Annavaram. "Origami Inference: Private Inference Using Hardware Enclaves." In 2021 IEEE 14th International Conference on Cloud Computing (CLOUD). IEEE, 2021. http://dx.doi.org/10.1109/cloud53861.2021.00021.

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Caticha, Ariel, Ali Mohammad-Djafari, Jean-François Bercher, and Pierre Bessiére. "Entropic Inference." In 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.

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Aiken, Alexander, and David Gay. "Barrier inference." In the 25th ACM SIGPLAN-SIGACT symposium. ACM Press, 1998. http://dx.doi.org/10.1145/268946.268974.

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Frank, Martin R., Piyawadee "Noi" Sukaviriya, and James D. Foley. "Inference bear." In the conference. ACM Press, 1995. http://dx.doi.org/10.1145/225434.225453.

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Reports on the topic "Inference"

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Kyburg Jr, Henry E. Probabilistic Inference and Non-Monotonic Inference. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada250603.

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Kyburg Jr, Henry E. Probabilistic Inference. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada255471.

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Gay, David. Barrier Inference. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada637072.

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Brandt, Sebastian, Anni-Yasmin Turhan, and Ralf Küsters. Foundations of non-standard inferences for DLs with transitive roles. Technische Universität Dresden, 2003. http://dx.doi.org/10.25368/2022.127.

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Description Logics (DLs) are a family of knowledge representation formalisms used for terminological reasoning. They have a wide range of applications such as medical knowledge-bases, or the semantic web. Research on DLs has been focused on the development of sound and complete inference algorithms to decide satisfiability and subsumption for increasingly expressive DLs. Non-standard inferences are a group of relatively new inference services which provide reasoning support for the building, maintaining, and deployment of DL knowledge-bases. So far, non-standard inferences are not available fo
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Warde, Cardinal. Optical Inference Machines. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada197880.

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Chertkov, Michael, Sungsoo Ahn, and Jinwoo Shin. Gauging Variational Inference. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1360686.

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Smith, David E., Michael R. Genesereth, and Matthew I. Ginsberg. Controlling Recursive Inference,. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada327440.

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Andrews, Isaiah, Toru Kitagawa, and Adam McCloskey. Inference on Winners. National Bureau of Economic Research, 2019. http://dx.doi.org/10.3386/w25456.

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McCloskey, Adam, Isaiah Andrews, and Toru Kitagawa. Inference on winners. The IFS, 2018. http://dx.doi.org/10.1920/wp.cem.2018.3118.

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Kitagawa, Toru, Isaiah Andrews, and Adam McCloskey. Inference on winners. The IFS, 2019. http://dx.doi.org/10.1920/wp.cem.2018.7318.

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