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Добірка наукової літератури з теми "Inference"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 1 серпня 2024

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Статті в журналах з теми "Inference"

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 (July 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 presented immediately before the sentences that were predicted to prompt them. In the inference condition, the target words were presented immediately after the sentences that were predicted to prompt the inferences. Results indicated that, for the high working memory capacity readers, lexical decision times were faster at the inference compared to the pre-inference locations for both types of inferences. In the case of low working capacity readers, lexical decision times were faster at the inference compared to the pre-inference locations only for reinstatement inferences. These findings suggest that working memory capacity plays a role in the making of causal inferences during the comprehension of natural texts.

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Wang, Yingxu. "Inference Algebra (IA)." International Journal of Cognitive Informatics and Natural Intelligence 6, no. 1 (January 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 inferences of IA are explored in three categories: a) Logical inferences; b) Analytic inferences; and c) Hybrid inferences. IA introduces the calculus of discrete causal differential and formal models of causations. IA enables artificial intelligence and computational intelligent systems to mimic human inference abilities by cognitive computing. A wide range of applications of IA are identified and demonstrated in cognitive informatics and computational intelligence towards novel theories and technologies for machine-enabled inferences and reasoning. This work is presented in two parts. The inference operators of IA as well as their extensions and applications will be presented in this paper; while the structure of formal inference, the framework of IA, and the mathematical models of formal causations has been published in the first part of the paper in IJCINI 5(4).

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Wang, Yingxu. "Inference Algebra (IA)." International Journal of Cognitive Informatics and Natural Intelligence 5, no. 4 (October 2011): 61–82. http://dx.doi.org/10.4018/jcini.2011100105.

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Анотація:

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 general functional, correlative, linear regression, and nonlinear regression causations; and c) Hybrid inferences on qualification and quantification causations. IA introduces a calculus of discrete causal differential and formal models of causations; based on them nine algebraic inference operators of IA are created for manipulating the formal causations. IA is one of the basic studies towards the next generation of intelligent computers known as cognitive computers. A wide range of applications of IA are identified and demonstrated in cognitive informatics and computational intelligence towards novel theories and technologies for machine-enabled inferences and reasoning.

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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 (May 18, 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, syntactically-driven focused inference relations, elaborate on their formal properties, and show that the series converges against System P. We take advantage of this result in form of an anytime algorithm for drawing inferences which is accompanied by a thorough complexity analysis.

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Starns, Jeffrey J., Andrea M. Cataldo, Caren M. Rotello, Jeffrey Annis, Andrew Aschenbrenner, Arndt Bröder, Gregory Cox, 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 (September 17, 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 cognitive psychology, we demonstrate a blinded-inference paradigm for assessing the quality of theoretical inferences from data. Our results reveal substantial variability in experts’ judgments on the very same data, hinting at a possible inference crisis.

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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 (November 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 inference that readers did or did not make. There were four types of experimental paragraphs: (1) Bridging inference, (2) Elaborative inference, (3) Word-Based Priming control, and (4) No Inference control. Participants were tested using the Daneman and Carpenter (1980) Reading Span Task and categorized as having low or high working memory capacity. The average peaks of the N400 component of the event-related brain potential (EM) were used as a measure of semantic priming and integration, such that the lower the N400 was in response to the explicitly stated inference concept, the more likely it was that the reader made the inference. Results indicate that readers with high working memory capacity made both bridging (necessary) and elaborative (optional) inferences during reading, whereas readers with low working memory capacity made only bridging inferences during reading. We interpret the findings within the framework of the Capacity Constrained Comprehension model of Just and Carpenter (1992).

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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 (August 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 inferences, and increasingly independent strategy use.ResultsThe treatment group demonstrated significantly superior performance on 1 of 2 measures of inference generation in reading and 1 measure of metacognitive ability compared with the control group. Significant differences between groups were not found on measures of reading comprehension or social inference ability.ConclusionThese findings suggest that the ACT & Check Strategy was effective in improving participants' ability to generate inferences in reading and certain metacognitive abilities, but the skills do not appear to generalize to other social communication contexts, such as social inference generation. This research provides a measure of support for explicitly teaching inference generation to address a reading inference deficit in adults with HF-ASD.

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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 (October 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 in the text. The effect of inference type, and condition on reading comprehension is discussed.

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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, particularly syntactic parse trees. New trees are generated by applying inference rules, which provide a unified representation for varying types of inferences. We use manual and automatic methods to generate these rules, which cover generic linguistic structures as well as specific lexical-based inferences. We also present a novel packed data-structure and a corresponding inference algorithm that allows efficient implementation of this formalism. We proved the correctness of the new algorithm and established its efficiency analytically and empirically. The utility of our approach was illustrated on two tasks: unsupervised relation extraction from a large corpus, and the Recognizing Textual Entailment (RTE) benchmarks.

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Landis, Christopher B., and Joshua A. Kroll. "Mitigating Inference Risks with the NIST Privacy Framework." Proceedings on Privacy Enhancing Technologies 2024, no. 1 (January 2024): 217–31. http://dx.doi.org/10.56553/popets-2024-0013.

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The NIST Privacy Framework describes itself as a comprehensive approach to organization-wide privacy program management. However, inferences can yield sensitive information of identities or attributes from nonsensitive information. Privacy governance must protect this information. Although many people and organizations are expanding their privacy definitions to include inferences, our gap analysis reveals that the framework's mapped controls are insufficient for managing inference-driven risk. The framework does not attend organizational focus to privacy inference risk sufficiently to support its stated claim of comprehensive risk management. Applying the framework to past incidents where ostensibly protected information was re-inferred, we analyze how organizations can better mitigate inference-based privacy violations. Finally, we recommend detailed improvements to the framework's controls to account better for inferences. Our recommendations encompass augmenting and mapping additional privacy risk controls to increase implementing organizations' awareness of inference risks, updating controls that depend on protecting specific PII categories, and enhancing organizations' proficiency in translating legal and policy requirements into technical implementations.

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Дисертації з теми "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.

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Анотація:

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.
Cataloged 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.

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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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Анотація:

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.
Cataloged 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.

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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 data. At the level of populations, we introduce a new inference method to estimate evolutionary distances for any two populations to their most recent common ancestral population using single-nucleotide polymorphism allele frequencies. Our method is based on a new evolutionary model for both drift and fixation. To scale this method to large numbers of populations, we introduce the asymmetric neighbor-joining algorithm, an efficient method for reconstructing rooted bifurcating trees. Asymmetric neighbor-joining provides a scalable rooting method applicable to any non-reversible evolutionary modelling setup. We explore the statistical properties of asymmetric neighbor-joining, and demonstrate its accuracy on synthetic data. We validate our method by reconstructing rooted phylogenetic trees from the Human Genome Diversity Panel data. Our results are obtained without using an outgroup, and are consistent with the prevalent recent single-origin model of human migration. At the level of species, we introduce a continuous time stochastic process, the geometric Poisson indel process, that allows indel rates to vary across sites. We design an efficient algorithm for computing the probability of a given multiple sequence alignment based on our new indel model. We describe a method to construct phylogeny estimates from a fixed alignment using neighbor-joining. Using simulation studies, we show that ignoring indel rate variation may have a detrimental effect on the accuracy of the inferred phylogenies, and that our proposed method can sidestep this issue by inferring latent indel rate categories. We also show that our phylogenetic inference method may be more stable to taxa subsampling in a real data experiment compared to some existing methods that either ignore indels or ignore indel rate variation.
Science, Faculty of
Statistics, Department of
Graduate

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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.
Includes 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

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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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Анотація:

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2014.
Cataloged 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.

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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 combinatorial optimization, related to the bipartite matching and assignment problem. After this, the implementation of the algorithm is described, followed by tests and a summary of achieved results.

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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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Книги з теми "Inference"

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

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Wieczorek, Wojciech. Grammatical Inference. Cham: 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. Berlin, Heidelberg: 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. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0575-7.

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

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

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

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

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Harney, Hanns L. Bayesian Inference. Berlin, Heidelberg: 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. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64919-7.

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Частини книг з теми "Inference"

Herkenhoff, Linda, and John Fogli. "Inference." In 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.

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Hooker, John N. "Inference." In Integrated Methods for Optimization, 223–369. Boston, MA: 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, 49–67. Boston, MA: 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, 983. New York, NY: 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, 137–61. Dordrecht: 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, 177–89. Berlin, Heidelberg: 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, 771. Boston, MA: 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, 1019–20. New York, NY: 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, 181–208. Cham: 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, 61–71. 2nd ed. New York: Routledge, 2023. http://dx.doi.org/10.4324/9781003387206-9.

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Тези доповідей конференцій з теми "Inference"

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}. California: 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. System W is a recently introduced inference system for nonmonotonic reasoning that captures and properly extends system Z as well as c-inference. We show that system W fulfils the syntax splitting postulates for inductive inference operators by showing that it satisfies the required properties of relevance and independence. This makes system W another inference operator besides c-inference that fully complies with syntax splitting, while in contrast to c-inference, also extending rational closure.

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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}. California: 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 comparing maximal consistent sets. And we show that it corresponds to the class of rational inference relations.

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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 these units should be thoroughly screened in the assembly line before they are deployed by the customers in the data centre. This paper talks about a generic Inference application that has been developed to execute inferences over multiple inputs for various real inference models and stress all the compute engines of the Inference chip. Inference outputs from a specific inference unit are stored and are assumed to be golden and further confirmed as golden statistically. Once the golden reference outputs are established, Inference application is deployed in the pre- and post-production environments to screen out defective units whose actual output do not match the reference. Strategy to compare against itself at mass scale resulted in achieving the Defects Per Million target for the customers.

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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 areas of didactic endeavour: teaching strategies to simplify the full complexity of inference by analogies, simulations, or visualisations on the one hand, and reduce the complexity of inference by a novel approach of Bootstrap and re-randomisation. The considerations about statistical inference will remain important in the era of Big Data. In this paper, the various approaches are compared for their merits and drawbacks.

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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. New York, New York, USA: 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. New York, New York, USA: ACM Press, 1995. http://dx.doi.org/10.1145/225434.225453.

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Mu, Weiyan, and Xiaona Yuan. "Statistical inference for ANOVA under heteroscedasticity: Statistical inference." In 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet). IEEE, 2012. http://dx.doi.org/10.1109/cecnet.2012.6201745.

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Звіти організацій з теми "Inference"

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

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

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

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Warde, Cardinal. Optical Inference Machines. Fort Belvoir, VA: Defense Technical Information Center, June 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), May 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,. Fort Belvoir, VA: Defense Technical Information Center, June 1985. http://dx.doi.org/10.21236/ada327440.

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

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

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Metu, Somiya, and Adrienne Raglin. Inference Model Documentation. DEVCOM Army Research Laboratory, September 2023. http://dx.doi.org/10.21236/ad1210687.

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