Academic literature on the topic 'Data Driven Inference'
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Journal articles on the topic "Data Driven Inference"
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.
Full textGREGOR, 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.
Full textLapalme, 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.
Full textGile, 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.
Full textWang, 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.
Full textAlmeida, 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.
Full textGile, 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.
Full textHansen, 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.
Full textBuscemi, 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.
Full textCattaneo, 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.
Full textDissertations / Theses on the topic "Data Driven Inference"
Park, June Young. "Data-driven Building Metadata Inference." Research Showcase @ CMU, 2016. http://repository.cmu.edu/theses/127.
Full textSpoon, Steven Alexander. "Demand-Driven Type Inference with Subgoal Pruning." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7486.
Full textMichelen, Strofer Carlos Alejandro. "Machine Learning and Field Inversion approaches to Data-Driven Turbulence Modeling." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103155.
Full textDoctor of Philosophy
The Reynolds-averaged Navier-Stokes (RANS) equations are widely used to simulate fluid flows in engineering applications despite their known inaccuracy in many flows of practical interest. The uncertainty in the RANS equations is known to stem from the Reynolds stress tensor for which no universally applicable turbulence model exists. The computational cost of more accurate methods for fluid flow simulation, however, means RANS simulations will likely continue to be a major tool in engineering applications and there is still a need for improved RANS turbulence modeling. This dissertation explores two different approaches to use available experimental data to improve RANS predictions by improving the uncertain Reynolds stress tensor field. The first approach is using machine learning to learn a data-driven turbulence model from a set of training data. This model can then be applied to predict new flows in place of traditional turbulence models. To this end, this dissertation presents a novel framework for training deep neural networks using experimental measurements of velocity and pressure. When using velocity and pressure data, gradient-based training of the neural network requires the sensitivity of the RANS equations to the learned Reynolds stress. Two different methods, the continuous adjoint and ensemble approximation, are used to obtain the required sensitivity. The second approach explored in this dissertation is field inversion, whereby available data for a flow of interest is used to infer a Reynolds stress field that leads to improved RANS solutions for that same flow. Here, the field inversion is done via the ensemble Kalman inversion (EKI), a Monte Carlo Bayesian procedure, and the focus is on improving the inference by enforcing known physical constraints on the inferred Reynolds stress field. To this end, a method for enforcing boundary conditions on the inferred field is presented. While further development is needed, the two data-driven approaches explored and improved upon here demonstrate the potential for improved practical RANS predictions.
Marcou, Quentin. "Probabilistic approaches to the adaptive immune repertoire : a data-driven approach." Thesis, Sorbonne Paris Cité, 2017. http://www.theses.fr/2017USPCB029/document.
Full textAn individual’s adaptive immune system needs to face repeated challenges of a constantly evolving environment with a virtually infinite number of threats. To achieve this task, the adaptive immune system relies on large diversity of B-cells and T-cells, each carrying a unique receptor specific to a small number of pathogens. These receptors are initially randomly built through the process of V(D)J recombination. This initial generated diversity is then narrowed down by a step of functional selection based on the receptors' folding properties and their ability to recognize self antigens. Upon recognition of a pathogen the B-cell will divide and its offsprings will undergo several rounds of successive somatic hypermutations and selection in an evolutionary process called affinity maturation. This work presents principled probabilistic approaches to infer the probability distribution underlying the recombination and somatic hypermutation processes from high throughput sequencing data using IGoR - a flexible software developed throughout the course of this PhD. IGoR has been developed as a versatile research tool and can encode a variety of models of different biological complexity to allow researchers in the field to characterize evermore precisely immune receptor repertoires. To motivate this data-driven approach we demonstrate that IGoR outperforms existing tools in accuracy and estimate the sample sizes needed for reliable repertoire characterization. Finally, using obtained model predictions, we show potential applications of these methods by demonstrating that homozygous twins share T-cells through cord blood, that the public core of the T cell repertoire is formed in the pre-natal period and finally estimate naive T cell clone lifetimes in human
Das, Debasish. "Bayesian Sparse Regression with Application to Data-driven Understanding of Climate." Diss., Temple University Libraries, 2015. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/313587.
Full textPh.D.
Sparse regressions based on constraining the L1-norm of the coefficients became popular due to their ability to handle high dimensional data unlike the regular regressions which suffer from overfitting and model identifiability issues especially when sample size is small. They are often the method of choice in many fields of science and engineering for simultaneously selecting covariates and fitting parsimonious linear models that are better generalizable and easily interpretable. However, significant challenges may be posed by the need to accommodate extremes and other domain constraints such as dynamical relations among variables, spatial and temporal constraints, need to provide uncertainty estimates and feature correlations, among others. We adopted a hierarchical Bayesian version of the sparse regression framework and exploited its inherent flexibility to accommodate the constraints. We applied sparse regression for the feature selection problem of statistical downscaling of the climate variables with particular focus on their extremes. This is important for many impact studies where the climate change information is required at a spatial scale much finer than that provided by the global or regional climate models. Characterizing the dependence of extremes on covariates can help in identification of plausible causal drivers and inform extremes downscaling. We propose a general-purpose sparse Bayesian framework for covariate discovery that accommodates the non-Gaussian distribution of extremes within a hierarchical Bayesian sparse regression model. We obtain posteriors over regression coefficients, which indicate dependence of extremes on the corresponding covariates and provide uncertainty estimates, using a variational Bayes approximation. The method is applied for selecting informative atmospheric covariates at multiple spatial scales as well as indices of large scale circulation and global warming related to frequency of precipitation extremes over continental United States. Our results confirm the dependence relations that may be expected from known precipitation physics and generates novel insights which can inform physical understanding. We plan to extend our model to discover covariates for extreme intensity in future. We further extend our framework to handle the dynamic relationship among the climate variables using a nonparametric Bayesian mixture of sparse regression models based on Dirichlet Process (DP). The extended model can achieve simultaneous clustering and discovery of covariates within each cluster. Moreover, the a priori knowledge about association between pairs of data-points is incorporated in the model through must-link constraints on a Markov Random Field (MRF) prior. A scalable and efficient variational Bayes approach is developed to infer posteriors on regression coefficients and cluster variables.
Temple University--Theses
Wu, Jinlong. "Predictive Turbulence Modeling with Bayesian Inference and Physics-Informed Machine Learning." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/85129.
Full textPh. D.
Reynolds-Averaged Navier–Stokes (RANS) simulations are widely used for engineering design and analysis involving turbulent flows. In RANS simulations, the Reynolds stress needs closure models and the existing models have large model-form uncertainties. Therefore, the RANS simulations are known to be unreliable in many flows of engineering relevance, including flows with three-dimensional structures, swirl, pressure gradients, or curvature. This lack of accuracy in complex flows has diminished the utility of RANS simulations as a predictive tool for engineering design, analysis, optimization, and reliability assessments. Recently, data-driven methods have emerged as a promising alternative to develop the model of Reynolds stress for RANS simulations. In this dissertation I explore two physics-informed, data-driven frameworks to improve RANS modeled Reynolds stresses. First, a Bayesian inference framework is proposed to quantify and reduce the model-form uncertainty of RANS modeled Reynolds stress by leveraging online sparse measurement data with empirical prior knowledge. Second, a machine-learning-assisted framework is proposed to utilize offline high fidelity simulation databases. Numerical results show that the data-driven RANS models have better prediction of Reynolds stress and other quantities of interest for several canonical flows. Two metrics are also presented for an a priori assessment of the prediction confidence for the machine-learning-assisted RANS model. The proposed data-driven methods are also applicable to the computational study of other physical systems whose governing equations have some unresolved physics to be modeled.
Koseler, Kaan Tamer. "Realization of Model-Driven Engineering for Big Data: A Baseball Analytics Use Case." Miami University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=miami1524832924255132.
Full textSušak, Hana 1985. "The Hunt of cancer genes : statistical inference of cancer risk and driver genes using next generation sequencuing data." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/668447.
Full textEls distints projectes internacionals de seqüenciació de càncer duts a terme en els últims anys han generat catàlegs complets d’alteracions trobades en els genomes tumorals, així com informació de variants germinals per a milers d'individus. En aquesta tesi descrivim dos mètodes estadístics aprofitant aquestes bases de dades per tal d’entendre millor la iniciació i la progressió dels tumors, i la contribució de variants genètiques al risc de desenvolupar càncer al llarg de la vida. El primer mètode, anomenat cDriver, es basa en un model d’inferència Bayesià que utilitza múltiples senyals de la selecció positiva que ocorre en els genomes tumorals per tal de predir els gens driver del càncer. En aquest mètode, hem inclòs la fracció de cèl·lules tumorals com a nova senyal de la selecció positiva a nivell cel·lular. Aquesta es basa en la hipòtesi que les cèl·lules que adquireixen mutacions ventajoses proliferaran i s’expandiran clonalment més ràpidament. Per avaluar cDriver, aquest es va comparar amb els mètodes més utilitzats per a la predicció de gens driver actuals. L’anàlisi es va dur a terme amb conjunts de dades de tres càncer diferents i els resultats van ser iguals o millors que els obtinguts per les eines més competitives en el tema. El segon mètode, anomenat REWAS, és un marc de treball per l’estudi d’associació de variants rares (RVAS) amb l'objectiu de millorar la identificació dels gens de predisposició al càncer. Tot i això, REWAS es pot aplicar a qualsevol estudi cas-control de malalties complexes. Per una altra part, a més d'integrar mètodes RVAS ben establerts, hem desenvolupat un nou mètode d'inferència Bayesiana RVAS basat en Integrated Nested Laplace Approximation (BATI). També demostrem que BATI mostra millors resultats que altres mètodes en dades simulades amb soroll de fons real, especialment quan el context biològic (p.e. variants amb impacte funcional) està disponible or quan les variants de risc expliquen en total poca variància fenotípica.
Sušak, Hana 1985. "The Hunt of cancer genes : statistical inference of cancer risk and driver genes using next generation sequencing data." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/664504.
Full textInternational cancer sequencing projects have generated comprehensive catalogs of alterations found in tumor genomes, as well as germline variant data for thousands of individuals. In this thesis, we describe two statistical methods exploiting these rich datasets in order to better understand tumor initiation, tumor progression and the contribution of genetic variants to the lifetime risk of developing cancer. The first method, a Bayesian inference model named cDriver, utilizes multiple signatures of positive selection acting on tumor genomes to predict cancer driver genes. Cancer cell fraction is introduced as a novel signature of positive selection on a cellular level, based on the hypothesis that cells obtaining additional advantageous driver mutations will undergo rapid proliferation and clonal expansion. We benchmarked cDriver against state of the art driver prediction methods on three cancer datasets demonstrating equal or better performance than the best competing tool. The second method, termed REWAS is a comprehensive framework for rare-variant association studies (RVAS) aiming at improving identification of cancer predisposition genes. Nonetheless, REWAS is readily applicable to any case-control study of complex diseases. Besides integrating well-established RVAS methods, we developed a novel Bayesian inference RVAS method (BATI) based on Integrated Nested Laplace Approximation (INLA). We demonstrate that BATI outperforms other methods on realistic simulated datasets, especially when meaningful biological context (e.g. functional impact of variants) is available or when risk variants in sum explain low phenotypic variance. Both methods developed during my thesis have the potential to facilitate personalized medicine and oncology through identification of novel therapeutic targets and identification of genetic predisposition facilitating prevention and early diagnosis of cancer.
Silva, Sanchez Rosa Elvira. "Contribution au pronostic de durée de vie des systèmes piles à combustible PEMFC." Thesis, Besançon, 2015. http://www.theses.fr/2015BESA2005/document.
Full textThis thesis work aims to provide solutions for the limited lifetime of Proton Exchange Membrane Fuel Cell Systems (PEM-FCS) based on two complementary disciplines:A first approach consists in increasing the lifetime of the PEM-FCS by designing and implementing a Prognostics & Health Management (PHM) architecture. The PEM-FCS are essentially multi-physical systems (electrical, fluid, electrochemical, thermal, mechanical, etc.) and multi-scale (time and space), thus its behaviors are hardly understandable. The nonlinear nature of phenomena, the reversibility or not of degradations and the interactions between components makes it quite difficult to have a failure modeling stage. Moreover, the lack of homogeneity (actual) in the manufacturing process makes it difficult for statistical characterization of their behavior. The deployment of a PHM solution would indeed anticipate and avoid failures, assess the state of health, estimate the Remaining Useful Lifetime (RUL) of the system and finally consider control actions (control and/or maintenance) to ensure operation continuity.A second approach proposes to use a passive hybridization of the PEMFC with Ultra Capacitors (UC) to operate the fuel cell closer to its optimum operating conditions and thereby minimize the impact of aging. The UC appear as an additional source to the PEMFC due to their high power density, their capacity to charge/discharge rapidly, their reversibility and their long life. If we take the example of fuel cell hybrid electrical vehicles, the association between a PEMFC and UC can be performed using a hybrid of active or passive type system. The overall behavior of the system depends on both, the choice of the architecture and the positioning of these elements in connection with the electric charge. Today, research in this area focuses mainly on energy management between the sources and embedded storage and the definition and optimization of a power electronic interface designated to adjust the flow of energy between them. However, the presence of power converters increases the source of faults and failures (failure of the switches of the power converter and the impact of high frequency current oscillations on the aging of the PEMFC), and also increases the energy losses of the entire system (even if the performance of the power converter is high, it nevertheless degrades the overall system)
Books on the topic "Data Driven Inference"
Sullivan, Ryan. Th e dangers of data-driven inference: The case of calendar effects in stock returns. London: London School of Economics, Financial Markets Group, 1998.
Find full textLi, Quan. Using R for Data Analysis in Social Sciences. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190656218.001.0001.
Full textBook chapters on the topic "Data Driven Inference"
Nickel, Soeren, and Martin Nöllenburg. "Towards Data-Driven Multilinear Metro Maps." In Diagrammatic Representation and Inference, 153–61. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54249-8_12.
Full textOncina, José. "The data driven approach applied to the OSTIA algorithm." In Grammatical Inference, 50–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0054063.
Full textRavela, Sai. "Statistical Inference for Coherent Fluids." In Dynamic Data-Driven Environmental Systems Science, 121–33. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25138-7_12.
Full textWandelt, Benjamin D., Jens Jasche, and Guilhem Lavaux. "Robust, Data-Driven Inference in Non-linear Cosmostatistics." In Lecture Notes in Statistics, 27–40. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3520-4_3.
Full textNikolopoulos, Spiros, Georgios Th Papadopoulos, Ioannis Kompatsiaris, and Ioannis Patras. "An Evidence-Driven Probabilistic Inference Framework for Semantic Image Understanding." In Machine Learning and Data Mining in Pattern Recognition, 525–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03070-3_40.
Full textMarkov, Zdravko, and Christo Dichev. "The net-clause language — A tool for data-driven inference." In Lecture Notes in Computer Science, 366–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/bfb0018453.
Full textShang, Chao. "Nonlinear Dynamic Soft Sensing Based on Bayesian Inference." In Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research, 125–40. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-6677-1_7.
Full textMiliauskaitė, Jolanta, and Diana Kalibatiene. "Complexity Issues in Data-Driven Fuzzy Inference Systems: Systematic Literature Review." In Communications in Computer and Information Science, 190–204. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57672-1_15.
Full textSalehnejad, Reza. "‘Homo economicus’ as an intuitive statistician (3): Data-driven causal inference." In Rationality, bounded rationality and microfoundations, 165–203. London: Palgrave Macmillan UK, 2007. http://dx.doi.org/10.1057/9780230625150_6.
Full textRentz, Niklas, Steven Smyth, Lewe Andersen, and Reinhard von Hanxleden. "Extracting Interactive Actor-Based Dataflow Models from Legacy C Code." In Diagrammatic Representation and Inference, 361–77. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86062-2_37.
Full textConference papers on the topic "Data Driven Inference"
Gokhale, Amruta, Daeyoung Kim, and Vinod Ganapathy. "Data-Driven Inference of API Mappings." In the 2nd Workshop. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2688471.2688480.
Full textMiltner, Anders, Saswat Padhi, Todd Millstein, and David Walker. "Data-driven inference of representation invariants." In PLDI '20: 41st ACM SIGPLAN International Conference on Programming Language Design and Implementation. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3385412.3385967.
Full textPadhi, Saswat, Rahul Sharma, and Todd Millstein. "Data-driven precondition inference with learned features." In PLDI '16: ACM SIGPLAN Conference on Programming Language Design and Implementation. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2908080.2908099.
Full textTan, Fei, Zhi Wei, Abhishek Pani, and Zhenyu Yan. "User Response Driven Content Understanding with Causal Inference." In 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 2019. http://dx.doi.org/10.1109/icdm.2019.00168.
Full textMani, Venugopal, Ramasubramanian Balasubramanian, Sushant Kumar, Abhinav Mathur, and Kannan Achan. "On Variational Inference for User Modeling in Attribute-Driven Collaborative Filtering." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9377927.
Full textBayramoglu, Neslihan, and A. Aydin Alatan. "Segmentation driven semantic information inference from 2.5D data." In 2009 IEEE 17th Signal Processing and Communications Applications Conference (SIU). IEEE, 2009. http://dx.doi.org/10.1109/siu.2009.5136468.
Full textGao, Jingkun, Joern Ploennigs, and Mario Berges. "A Data-driven Meta-data Inference Framework for Building Automation Systems." In BuildSys '15: The 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2821650.2821670.
Full textRomer, Anne, Jan Maximilian Montenbruck, and Frank Allgower. "Sampling strategies for data-driven inference of passivity properties." In 2017 IEEE 56th Annual Conference on Decision and Control (CDC). IEEE, 2017. http://dx.doi.org/10.1109/cdc.2017.8264623.
Full textSharma, Arun K., Dhanjeet Singh, and Nishchal K. Verma. "Data Driven Aerodynamic Modeling Using Mamdani Fuzzy Inference Systems." In 2018 International Conference on Sensing,Diagnostics, Prognostics, and Control (SDPC). IEEE, 2018. http://dx.doi.org/10.1109/sdpc.2018.8664870.
Full textTaibi, Imane, Yassine Hadjadj-Aoul, and Chadi Barakat. "Data Driven Network Performance Inference From Within The Browser." In 2020 IEEE Symposium on Computers and Communications (ISCC). IEEE, 2020. http://dx.doi.org/10.1109/iscc50000.2020.9219573.
Full text