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Journal articles on the topic 'Data Driven Inference'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Padhi, Saswat, Rahul Sharma, and Todd Millstein. "Data-driven precondition inference with learned features." ACM SIGPLAN Notices 51, no. 6 (August 2016): 42–56. http://dx.doi.org/10.1145/2980983.2908099.

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2

GREGOR, J. "DATA-DRIVEN INDUCTIVE INFERENCE OF FINITE-STATE AUTOMATA." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 01 (February 1994): 305–22. http://dx.doi.org/10.1142/s0218001494000140.

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Within the field of structural pattern analysis, algorithms for inference of discrete mathematical models from samples are an important area of research. This paper gives an extensive survey of state-of-the-art methods for data-driven inductive inference of finite-state automata. In addition to providing notationally consistent descriptions of the methods’ fundamental mode of operation, aspects such as sequential learning, advantages and disadvantages, and the extension to stochastic automata are also addressed.

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3

Lapalme, Ervig, Jean-Marc Lina, and Jérémie Mattout. "Data-driven parceling and entropic inference in MEG." NeuroImage 30, no. 1 (March 2006): 160–71. http://dx.doi.org/10.1016/j.neuroimage.2005.08.067.

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4

Gile, Krista J., Isabelle S. Beaudry, Mark S. Handcock, and Miles Q. Ott. "Methods for Inference from Respondent-Driven Sampling Data." Annual Review of Statistics and Its Application 5, no. 1 (March 7, 2018): 65–93. http://dx.doi.org/10.1146/annurev-statistics-031017-100704.

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5

Wang, Dan, Shiguang Shan, Hongming Zhang, Wei Zeng, and Xilin Chen. "Data-driven hair segmentation with isomorphic manifold inference." Image and Vision Computing 32, no. 10 (October 2014): 739–50. http://dx.doi.org/10.1016/j.imavis.2014.02.011.

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6

Almeida, Alexandre CL, Anderson R. Duarte, Luiz H. Duczmal, Fernando LP Oliveira, and Ricardo HC Takahashi. "Data-driven inference for the spatial scan statistic." International Journal of Health Geographics 10, no. 1 (2011): 47. http://dx.doi.org/10.1186/1476-072x-10-47.

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7

Gile, Krista J., and Mark S. Handcock. "Network model-assisted inference from respondent-driven sampling data." Journal of the Royal Statistical Society: Series A (Statistics in Society) 178, no. 3 (January 27, 2015): 619–39. http://dx.doi.org/10.1111/rssa.12091.

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8

Hansen, Sofie Therese, Søren Hauberg, and Lars Kai Hansen. "Data-driven forward model inference for EEG brain imaging." NeuroImage 139 (October 2016): 249–58. http://dx.doi.org/10.1016/j.neuroimage.2016.06.017.

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9

Buscemi, Francesco, and Michele Dall’Arno. "Data-driven inference of physical devices: theory and implementation." New Journal of Physics 21, no. 11 (November 14, 2019): 113029. http://dx.doi.org/10.1088/1367-2630/ab5003.

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10

Cattaneo, Matias D., Richard K. Crump, and Michael Jansson. "Robust Data-Driven Inference for Density-Weighted Average Derivatives." Journal of the American Statistical Association 105, no. 491 (September 1, 2010): 1070–83. http://dx.doi.org/10.1198/jasa.2010.tm09590.

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11

Calonico, Sebastian, Matias D. Cattaneo, and Rocío Titiunik. "Robust Data-Driven Inference in the Regression-Discontinuity Design." Stata Journal: Promoting communications on statistics and Stata 14, no. 4 (December 2014): 909–46. http://dx.doi.org/10.1177/1536867x1401400413.

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12

Eguchi, Shoichi, and Hiroki Masuda. "Data driven time scale in Gaussian quasi-likelihood inference." Statistical Inference for Stochastic Processes 22, no. 3 (March 16, 2019): 383–430. http://dx.doi.org/10.1007/s11203-019-09197-x.

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13

Persson, Emma, Jenny Häggström, Ingeborg Waernbaum, and Xavier de Luna. "Data-driven algorithms for dimension reduction in causal inference." Computational Statistics & Data Analysis 105 (January 2017): 280–92. http://dx.doi.org/10.1016/j.csda.2016.08.012.

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14

Peignier, Sergio, Pauline Schmitt, and Federica Calevro. "Data-driven Gene Regulatory Networks Inference Based on Classification Algorithms." International Journal on Artificial Intelligence Tools 30, no. 04 (June 2021): 2150022. http://dx.doi.org/10.1142/s0218213021500226.

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Inferring Gene Regulatory Networks from high-throughput gene expression data is a challenging problem, addressed by the systems biology community. Most approaches that aim at unraveling the gene regulation mechanisms in a data-driven way, analyze gene expression datasets to score potential regulatory links between transcription factors and target genes. So far, three major families of approaches have been proposed to score regulatory links. These methods rely respectively on correlation measures, mutual information metrics, and regression algorithms. In this paper we present a new family of data-driven inference methods. This new family, inspired by the regression-based paradigm, relies on the use of classification algorithms. This paper assesses and advocates for the use of this paradigm as a new promising approach to infer gene regulatory networks. Indeed, the development and assessment of five new inference methods based on well-known classification algorithms shows that the classification-based inference family exhibits good results when compared to well-established paradigms.

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15

Li, Hongsong, Kenny Q. Zhu, and Haixun Wang. "Data-Driven Metaphor Recognition and Explanation." Transactions of the Association for Computational Linguistics 1 (December 2013): 379–90. http://dx.doi.org/10.1162/tacl_a_00235.

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Recognizing metaphors and identifying the source-target mappings is an important task as metaphorical text poses a big challenge for machine reading. To address this problem, we automatically acquire a metaphor knowledge base and an isA knowledge base from billions of web pages. Using the knowledge bases, we develop an inference mechanism to recognize and explain the metaphors in the text. To our knowledge, this is the first purely data-driven approach of probabilistic metaphor acquisition, recognition, and explanation. Our results shows that it significantly outperforms other state-of-the-art methods in recognizing and explaining metaphors.

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16

Mehta, Piyush M., Richard Linares, and Eric K. Sutton. "Data‐Driven Inference of Thermosphere Composition During Solar Minimum Conditions." Space Weather 17, no. 9 (September 2019): 1364–79. http://dx.doi.org/10.1029/2019sw002264.

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17

Bellegarda, J. R., and K. E. A. Silverman. "Natural language spoken interface control using data-driven semantic inference." IEEE Transactions on Speech and Audio Processing 11, no. 3 (May 2003): 267–77. http://dx.doi.org/10.1109/tsa.2003.811534.

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18

Eduati, Federica, Javier De Las Rivas, Barbara Di Camillo, Gianna Toffolo, and Julio Saez-Rodriguez. "Integrating literature-constrained and data-driven inference of signalling networks." Bioinformatics 28, no. 18 (June 25, 2012): 2311–17. http://dx.doi.org/10.1093/bioinformatics/bts363.

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19

Romer, Anne, Jan Maximilian Montenbruck, and Frank Allgöwer. "Data-driven inference of conic relations via saddle-point dynamics." IFAC-PapersOnLine 51, no. 25 (2018): 396–401. http://dx.doi.org/10.1016/j.ifacol.2018.11.139.

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20

Peherstorfer, Benjamin, and Karen Willcox. "Data-driven operator inference for nonintrusive projection-based model reduction." Computer Methods in Applied Mechanics and Engineering 306 (July 2016): 196–215. http://dx.doi.org/10.1016/j.cma.2016.03.025.

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21

Shu, Yidan, and Jinsong Zhao. "Data-driven causal inference based on a modified transfer entropy." Computers & Chemical Engineering 57 (October 2013): 173–80. http://dx.doi.org/10.1016/j.compchemeng.2013.05.011.

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22

Hubbard, Alan E., Sara Kherad-Pajouh, and Mark J. van der Laan. "Statistical Inference for Data Adaptive Target Parameters." International Journal of Biostatistics 12, no. 1 (May 1, 2016): 3–19. http://dx.doi.org/10.1515/ijb-2015-0013.

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Abstract Consider one observes n i.i.d. copies of a random variable with a probability distribution that is known to be an element of a particular statistical model. In order to define our statistical target we partition the sample in V equal size sub-samples, and use this partitioning to define V splits in an estimation sample (one of the V subsamples) and corresponding complementary parameter-generating sample. For each of the V parameter-generating samples, we apply an algorithm that maps the sample to a statistical target parameter. We define our sample-split data adaptive statistical target parameter as the average of these V-sample specific target parameters. We present an estimator (and corresponding central limit theorem) of this type of data adaptive target parameter. This general methodology for generating data adaptive target parameters is demonstrated with a number of practical examples that highlight new opportunities for statistical learning from data. This new framework provides a rigorous statistical methodology for both exploratory and confirmatory analysis within the same data. Given that more research is becoming “data-driven”, the theory developed within this paper provides a new impetus for a greater involvement of statistical inference into problems that are being increasingly addressed by clever, yet ad hoc pattern finding methods. To suggest such potential, and to verify the predictions of the theory, extensive simulation studies, along with a data analysis based on adaptively determined intervention rules are shown and give insight into how to structure such an approach. The results show that the data adaptive target parameter approach provides a general framework and resulting methodology for data-driven science.

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23

Gjerga, Enio, Panuwat Trairatphisan, Attila Gabor, and Julio Saez-Rodriguez. "Literature and data-driven based inference of signalling interactions using time-course data." IFAC-PapersOnLine 52, no. 26 (2019): 52–57. http://dx.doi.org/10.1016/j.ifacol.2019.12.235.

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24

Morningstar, Warren R., Laurence Perreault Levasseur, Yashar D. Hezaveh, Roger Blandford, Phil Marshall, Patrick Putzky, Thomas D. Rueter, Risa Wechsler, and Max Welling. "Data-driven Reconstruction of Gravitationally Lensed Galaxies Using Recurrent Inference Machines." Astrophysical Journal 883, no. 1 (September 17, 2019): 14. http://dx.doi.org/10.3847/1538-4357/ab35d7.

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25

Chen, Haipeng, Mohammad T. Hajiaghayi, Sarit Kraus, Anshul Sawant, Edoardo Serra, V. S. Subrahmanian, and Yanhai Xiong. "PIE: A Data-Driven Payoff Inference Engine for Strategic Security Applications." IEEE Transactions on Computational Social Systems 7, no. 1 (February 2020): 42–57. http://dx.doi.org/10.1109/tcss.2019.2957178.

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26

Fujii, Keisuke, Ichihiro Yamada, and Masahiro Hasuo. "Data-driven sensitivity inference for Thomson scattering electron density measurement systems." Review of Scientific Instruments 88, no. 1 (January 2017): 013508. http://dx.doi.org/10.1063/1.4974344.

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27

Iddianozie, Chidubem, Michela Bertolotto, and Gavin Mcardle. "Exploring Budgeted Learning for Data-Driven Semantic Inference via Urban Functions." IEEE Access 8 (2020): 32258–69. http://dx.doi.org/10.1109/access.2020.2973885.

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28

Menendez, David, and Santosh Nagarakatte. "Alive-Infer: data-driven precondition inference for peephole optimizations in LLVM." ACM SIGPLAN Notices 52, no. 6 (September 14, 2017): 49–63. http://dx.doi.org/10.1145/3140587.3062372.

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29

Wu, Junfeng, Yuzhe Li, Daniel E. Quevedo, Vincent Lau, and Ling Shi. "Data-driven power control for state estimation: A Bayesian inference approach." Automatica 54 (April 2015): 332–39. http://dx.doi.org/10.1016/j.automatica.2015.02.019.

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30

Adnan, Liang, El-Shafie, Zounemat-Kermani, and Kisi. "Prediction of Suspended Sediment Load Using Data-Driven Models." Water 11, no. 10 (October 2, 2019): 2060. http://dx.doi.org/10.3390/w11102060.

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Estimation of suspended sediments carried by natural rivers is essential for projects related to water resource planning and management. This study proposes a dynamic evolving neural fuzzy inference system (DENFIS) as an alternative tool to estimate the suspended sediment load based on previous values of streamflow and sediment. Several input scenarios of daily streamflow and suspended sediment load measured at two locations of China—Guangyuan and Beibei—were tried to assess the ability of this new method and its results were compared with those of the other two common methods, adaptive neural fuzzy inference system with fuzzy c-means clustering (ANFIS-FCM) and multivariate adaptive regression splines (MARS) based on three commonly utilized statistical indices, root mean square error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE). The data period covers 01/04/2007–12/31/2015 for the both stations. A comparison of the methods indicated that the DENFIS-based models improved the accuracy of the ANFIS-FCM and MARS-based models with respect to RMSE by 33% (32%) and 31% (36%) for the Guangyuan (Beibei) station, respectively. The NSE accuracy for ANFIS-FCM and MARS-based models were increased by 4% (36%) and 15% (19%) using DENFIS for the Guangyuan (Beibei) station, respectively. It was found that the suspended sediment load can be accurately estimated by DENFIS-based models using only previous streamflow data.

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31

Antu, Jannatul Ferdous, Sabina Sharmin, and Taslim Sazzad Mallick. "Generalized Quasilikelihood Inference for Zero Inflated Longitudinal Count Data." Dhaka University Journal of Science 68, no. 1 (January 30, 2020): 95–99. http://dx.doi.org/10.3329/dujs.v68i1.54602.

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In this paper, we extend an observation-driven model for time series of zero inflated count data to longitudinal data setup. Basic properties of the models are discussed. For statistical inference of the proposed model, a generalized quasilikelihood (GQL) estimating equation has been derived for the regression parameter. A pharmaceutical data has been reanalyzed using the proposed approach and results are compared. The proposed approach produces similar estimates as given in the earlier work with much smaller standard errors. Dhaka Univ. J. Sci. 68(1): 95-99, 2020 (January)

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32

Raman, Natraj, and Jochen Leidner. "Municipal Bond Pricing: A Data Driven Method." International Journal of Financial Studies 6, no. 3 (September 11, 2018): 80. http://dx.doi.org/10.3390/ijfs6030080.

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Price evaluations of municipal bonds have traditionally been performed by human experts based on their market knowledge and trading experience. Automated evaluation is an attractive alternative providing the advantage of an objective estimation that is transparent, consistent, and scalable. In this paper, we present a statistical model to automatically estimate U.S municipal bond yields based on trade transactions and study the agreement between human evaluations and machine generated estimates. The model uses piecewise polynomials constructed using basis functions. This provides immense flexibility in capturing the wide dispersion of yields. A novel transfer learning based approach that exploits the latent hierarchical relationship of the bonds is applied to enable robust yield estimation even in the absence of adequate trade data. The Bayesian nature of our model offers a principled framework to account for uncertainty in the estimates. Our inference procedure scales well even for large data sets. We demonstrate the empirical effectiveness of our model by assessing over 100,000 active bonds and find that our estimates are in line with hand priced evaluations for a large number of bonds.

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33

Chen, Yue, Yu-Wang Chen, Xiao-Bin Xu, Chang-Chun Pan, Jian-Bo Yang, and Gen-Ke Yang. "A data-driven approximate causal inference model using the evidential reasoning rule." Knowledge-Based Systems 88 (November 2015): 264–72. http://dx.doi.org/10.1016/j.knosys.2015.07.026.

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34

Escolar-Jimenez, Caryl Charlene. "Data-Driven Decisions in Employee Compensation utilizing a Neuro-Fuzzy Inference System." International Journal of Emerging Trends in Engineering Research 7, no. 8 (August 25, 2019): 163–69. http://dx.doi.org/10.30534/ijeter/2019/10782019.

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35

Bartoletti, N., F. Casagli, S. Marsili-Libelli, A. Nardi, and L. Palandri. "Data-driven rainfall/runoff modelling based on a neuro-fuzzy inference system." Environmental Modelling & Software 106 (August 2018): 35–47. http://dx.doi.org/10.1016/j.envsoft.2017.11.026.

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36

Meng, B., R. C. G. M. Loonen, and J. L. M. Hensen. "Data-driven inference of unknown tilt and azimuth of distributed PV systems." Solar Energy 211 (November 2020): 418–32. http://dx.doi.org/10.1016/j.solener.2020.09.077.

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37

Chakraborty, Bibhas, Eric B. Laber, and Ying-Qi Zhao. "Inference about the expected performance of a data-driven dynamic treatment regime." Clinical Trials: Journal of the Society for Clinical Trials 11, no. 4 (June 12, 2014): 408–17. http://dx.doi.org/10.1177/1740774514537727.

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38

Frolov, Nikita, Vladimir Maksimenko, Annika Lüttjohann, Alexey Koronovskii, and Alexander Hramov. "Feed-forward artificial neural network provides data-driven inference of functional connectivity." Chaos: An Interdisciplinary Journal of Nonlinear Science 29, no. 9 (September 2019): 091101. http://dx.doi.org/10.1063/1.5117263.

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39

Sun, Weigao, Mohsen Zamani, Mohammad Reza Hesamzadeh, and Hai-Tao Zhang. "Data-Driven Probabilistic Optimal Power Flow With Nonparametric Bayesian Modeling and Inference." IEEE Transactions on Smart Grid 11, no. 2 (March 2020): 1077–90. http://dx.doi.org/10.1109/tsg.2019.2931160.

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40

Sharf, Miel. "On the Sample Complexity of Data-Driven Inference of the L2-Gain." IEEE Control Systems Letters 4, no. 4 (October 2020): 904–9. http://dx.doi.org/10.1109/lcsys.2020.2996581.

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41

Haigh, Matthew, and Jean-François Bonnefon. "Eye Movements Reveal How Readers Infer Intentions From the Beliefs and Desires of Others." Experimental Psychology 62, no. 3 (May 7, 2015): 206–13. http://dx.doi.org/10.1027/1618-3169/a000290.

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We examine how the beliefs and desires of a protagonist are used by readers to predict their intentions as a narrative vignette unfolds. Eye movement measures revealed that readers rapidly inferred an intention when the protagonist desired an outcome, even when this inference was not licensed by the protagonist’s belief state. Reading was immediately disrupted when participants encountered a described action that contradicted this inference. During intermediate processing, desire inferences were moderated by the protagonist’s belief state. Effects that emerged later in the text were again driven solely by the protagonist’s desires. These data suggest that desire-based inferences are initially drawn irrespective of belief state, but are then quickly inhibited if not licensed by relevant beliefs. This inhibition of desire-based inferences may be an effortful process as it was not systematically sustained in later steps of processing.

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42

Nishiyama, Yu, Motonobu Kanagawa, Arthur Gretton, and Kenji Fukumizu. "Model-based kernel sum rule: kernel Bayesian inference with probabilistic models." Machine Learning 109, no. 5 (January 2, 2020): 939–72. http://dx.doi.org/10.1007/s10994-019-05852-9.

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AbstractKernel Bayesian inference is a principled approach to nonparametric inference in probabilistic graphical models, where probabilistic relationships between variables are learned from data in a nonparametric manner. Various algorithms of kernel Bayesian inference have been developed by combining kernelized basic probabilistic operations such as the kernel sum rule and kernel Bayes’ rule. However, the current framework is fully nonparametric, and it does not allow a user to flexibly combine nonparametric and model-based inferences. This is inefficient when there are good probabilistic models (or simulation models) available for some parts of a graphical model; this is in particular true in scientific fields where “models” are the central topic of study. Our contribution in this paper is to introduce a novel approach, termed the model-based kernel sum rule (Mb-KSR), to combine a probabilistic model and kernel Bayesian inference. By combining the Mb-KSR with the existing kernelized probabilistic rules, one can develop various algorithms for hybrid (i.e., nonparametric and model-based) inferences. As an illustrative example, we consider Bayesian filtering in a state space model, where typically there exists an accurate probabilistic model for the state transition process. We propose a novel filtering method that combines model-based inference for the state transition process and data-driven, nonparametric inference for the observation generating process. We empirically validate our approach with synthetic and real-data experiments, the latter being the problem of vision-based mobile robot localization in robotics, which illustrates the effectiveness of the proposed hybrid approach.

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43

Yadav, Deepesh, and Sudhirkumar V. Barai. "FUZZY INFERENCE DRIVEN INTERNET BASED BRIDGE MANAGEMENT SYSTEM." TRANSPORT 20, no. 1 (February 28, 2005): 37–44. http://dx.doi.org/10.3846/16484142.2005.9637993.

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Bridges form an integral part of any country's transportation scheme, be it a railway network or a highway network. Periodic evaluation and subsequent maintenance can sometimes increase the life of a bridge to a great extent. Quantitative evaluation, however accurate, is too cumbersome, hence, in the present system the procedure of inspection is standardized and the analysis is done qualitatively. The qualitative data are converted to a mathematical format for the assessment by the use of popular Fuzzy logic approach. The whole system is based on the principle of evaluation from parts to the whole bridge structure. The method of calculation is based on Vertex method for fuzzy functions. The prototype system is the Internet based systemand it can be accessed from anywhere as long as the area has the Internet access. The prototype system is an attempt in the direction of an organized and well‐documented bridge management system.

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44

Frosch, Caren A., C. Philip Beaman, and Rachel Mccloy. "A Little Learning is a Dangerous Thing: An Experimental Demonstration of Ignorance-Driven Inference." Quarterly Journal of Experimental Psychology 60, no. 10 (October 2007): 1329–36. http://dx.doi.org/10.1080/17470210701507949.

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Studies of ignorance-driven decision making have been employed to analyse when ignorance should prove advantageous on theoretical grounds or else they have been employed to examine whether human behaviour is consistent with an ignorance-driven inference strategy (e.g., the recognition heuristic). In the current study we examine whether—under conditions where such inferences might be expected—the advantages that theoretical analyses predict are evident in human performance data. A single experiment shows that, when asked to make relative wealth judgements, participants reliably use recognition as a basis for their judgements. Their wealth judgements under these conditions are reliably more accurate when some of the target names are unknown than when participants recognize all of the names (a “less-is-more effect”). These results are consistent across a number of variations: the number of options given to participants and the nature of the wealth judgement. A basic model of recognition-based inference predicts these effects.

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45

Azizi, Elham, Sandhya Prabhakaran, Ambrose Carr, and Dana Pe'er. "Bayesian Inference for Single-cell Clustering and Imputing." Genomics and Computational Biology 3, no. 1 (January 26, 2017): 46. http://dx.doi.org/10.18547/gcb.2017.vol3.iss1.e46.

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Single-cell RNA-seq gives access to gene expression measurements for thousands of cells, allowing discovery and characterization of cell types. However, the data is noise-prone due to experimental errors and cell type-specific biases. Current computational approaches for analyzing single-cell data involve a global normalization step which introduces incorrect biases and spurious noise and does not resolve missing data (dropouts). This can lead to misleading conclusions in downstream analyses. Moreover, a single normalization removes important cell type-specific information. We propose a data-driven model, BISCUIT, that iteratively normalizes and clusters cells, thereby separating noise from interesting biological signals. BISCUIT is a Bayesian probabilistic model that learns cell-specific parameters to intelligently drive normalization. This approach displays superior performance to global normalization followed by clustering in both synthetic and real single-cell data compared with previous methods, and allows easy interpretation and recovery of the underlying structure and cell types.

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46

Natarajan, Ranjini, and Charles E. McCulloch. "Gibbs Sampling with Diffuse Proper Priors: A Valid Approach to Data-Driven Inference?" Journal of Computational and Graphical Statistics 7, no. 3 (September 1998): 267. http://dx.doi.org/10.2307/1390704.

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47

Gile, Krista J. "Improved Inference for Respondent-Driven Sampling Data With Application to HIV Prevalence Estimation." Journal of the American Statistical Association 106, no. 493 (March 2011): 135–46. http://dx.doi.org/10.1198/jasa.2011.ap09475.

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48

Natarajan, Ranjini, and Charles E. McCulloch. "Gibbs Sampling with Diffuse Proper Priors: A Valid Approach to Data-Driven Inference?" Journal of Computational and Graphical Statistics 7, no. 3 (September 1998): 267–77. http://dx.doi.org/10.1080/10618600.1998.10474776.

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49

Zhai, Yanwei, Zheng Lv, Jun Zhao, Wei Wang, and Henry Leung. "Data-driven inference modeling based on an on-line Wang-Mendel fuzzy approach." Information Sciences 551 (April 2021): 113–27. http://dx.doi.org/10.1016/j.ins.2020.10.018.

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50

Jasra, Ajay, Kengo Kamatani, and Hiroki Masuda. "Bayesian inference for stable Lévy–driven stochastic differential equations with high‐frequency data." Scandinavian Journal of Statistics 46, no. 2 (November 20, 2018): 545–74. http://dx.doi.org/10.1111/sjos.12362.

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