Bibliografias temáticas / Understanding of data models

Literatura científica selecionada sobre o tema "Understanding of data models"

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Publicado: 17 de julho de 2024

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Artigos de revistas sobre o assunto "Understanding of data models"

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Shanks, Graeme, e Peta Darke. "Understanding corporate data models". Information & Management 35, n.º 1 (janeiro de 1999): 19–30. http://dx.doi.org/10.1016/s0378-7206(98)00078-0.

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French, Robert M., e Maud Jacquet. "Understanding bilingual memory: models and data". Trends in Cognitive Sciences 8, n.º 2 (fevereiro de 2004): 87–93. http://dx.doi.org/10.1016/j.tics.2003.12.011.

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DeBruine, Lisa M., e Dale J. Barr. "Understanding Mixed-Effects Models Through Data Simulation". Advances in Methods and Practices in Psychological Science 4, n.º 1 (janeiro de 2021): 251524592096511. http://dx.doi.org/10.1177/2515245920965119.

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Experimental designs that sample both subjects and stimuli from a larger population need to account for random effects of both subjects and stimuli using mixed-effects models. However, much of this research is analyzed using analysis of variance on aggregated responses because researchers are not confident specifying and interpreting mixed-effects models. This Tutorial explains how to simulate data with random-effects structure and analyze the data using linear mixed-effects regression (with the lme4 R package), with a focus on interpreting the output in light of the simulated parameters. Data simulation not only can enhance understanding of how these models work, but also enables researchers to perform power calculations for complex designs. All materials associated with this article can be accessed at https://osf.io/3cz2e/ .
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Knüsel, Benedikt, e Christoph Baumberger. "Understanding climate phenomena with data-driven models". Studies in History and Philosophy of Science Part A 84 (dezembro de 2020): 46–56. http://dx.doi.org/10.1016/j.shpsa.2020.08.003.

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Durant, Szonya. "Zhaoping, L. Understanding Vision: Theory, Models, and Data". Perception 45, n.º 10 (19 de julho de 2016): 1207–8. http://dx.doi.org/10.1177/0301006616660638.

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Best, Nicky, e Peter Green. "Structure and uncertainty: Graphical models for understanding complex data". Significance 2, n.º 4 (30 de novembro de 2005): 177–81. http://dx.doi.org/10.1111/j.1740-9713.2005.00133.x.

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Steinberg, David M., e Dizza Bursztyn. "Data Analytic Tools for Understanding Random Field Regression Models". Technometrics 46, n.º 4 (novembro de 2004): 411–20. http://dx.doi.org/10.1198/004017004000000419.

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Cagetti, Marco, e Mariacristina De Nardi. "WEALTH INEQUALITY: DATA AND MODELS". Macroeconomic Dynamics 12, S2 (setembro de 2008): 285–313. http://dx.doi.org/10.1017/s1365100507070150.

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In the United States wealth is highly concentrated and very unequally distributed: the richest 1% hold one third of the total wealth in the economy. Understanding the determinants of wealth inequality is a challenge for many economic models. We summarize some key facts about the wealth distribution and what economic models have been able to explain so far.
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Butts, Daniel A. "Data-Driven Approaches to Understanding Visual Neuron Activity". Annual Review of Vision Science 5, n.º 1 (15 de setembro de 2019): 451–77. http://dx.doi.org/10.1146/annurev-vision-091718-014731.

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With modern neurophysiological methods able to record neural activity throughout the visual pathway in the context of arbitrarily complex visual stimulation, our understanding of visual system function is becoming limited by the available models of visual neurons that can be directly related to such data. Different forms of statistical models are now being used to probe the cellular and circuit mechanisms shaping neural activity, understand how neural selectivity to complex visual features is computed, and derive the ways in which neurons contribute to systems-level visual processing. However, models that are able to more accurately reproduce observed neural activity often defy simple interpretations. As a result, rather than being used solely to connect with existing theories of visual processing, statistical modeling will increasingly drive the evolution of more sophisticated theories.
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Yoo, Kang Min, Youhyun Shin e Sang-goo Lee. "Data Augmentation for Spoken Language Understanding via Joint Variational Generation". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julho de 2019): 7402–9. http://dx.doi.org/10.1609/aaai.v33i01.33017402.

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Data scarcity is one of the main obstacles of domain adaptation in spoken language understanding (SLU) due to the high cost of creating manually tagged SLU datasets. Recent works in neural text generative models, particularly latent variable models such as variational autoencoder (VAE), have shown promising results in regards to generating plausible and natural sentences. In this paper, we propose a novel generative architecture which leverages the generative power of latent variable models to jointly synthesize fully annotated utterances. Our experiments show that existing SLU models trained on the additional synthetic examples achieve performance gains. Our approach not only helps alleviate the data scarcity issue in the SLU task for many datasets but also indiscriminately improves language understanding performances for various SLU models, supported by extensive experiments and rigorous statistical testing.
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Teses / dissertações sobre o assunto "Understanding of data models"

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Sommeria-Klein, Guilhem. "From models to data : understanding biodiversity patterns from environmental DNA data". Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30390/document.

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La distribution de l'abondance des espèces en un site, et la similarité de la composition taxonomique d'un site à l'autre, sont deux mesures de la biodiversité ayant servi de longue date de base empirique aux écologues pour tenter d'établir les règles générales gouvernant l'assemblage des communautés d'organismes. Pour ce type de mesures intégratives, le séquençage haut-débit d'ADN prélevé dans l'environnement (" ADN environnemental ") représente une alternative récente et prometteuse aux observations naturalistes traditionnelles. Cette approche présente l'avantage d'être rapide et standardisée, et donne accès à un large éventail de taxons microbiens jusqu'alors indétectables. Toutefois, ces jeux de données de grande taille à la structure complexe sont difficiles à analyser, et le caractère indirect des observations complique leur interprétation. Le premier objectif de cette thèse est d'identifier les modèles statistiques permettant d'exploiter ce nouveau type de données afin de mieux comprendre l'assemblage des communautés. Le deuxième objectif est de tester les approches retenues sur des données de biodiversité du sol en forêt amazonienne, collectées en Guyane française. Deux grands types de processus sont invoqués pour expliquer l'assemblage des communautés d'organismes : les processus "neutres", indépendants de l'espèce considérée, que sont la naissance, la mort et la dispersion des organismes, et les processus liés à la niche écologique occupée par les organismes, c'est-à-dire les interactions avec l'environnement et entre organismes. Démêler l'importance relative de ces deux types de processus dans l'assemblage des communautés est une question fondamentale en écologie ayant de nombreuses implications, notamment pour l'estimation de la biodiversité et la conservation. Le premier chapitre aborde cette question à travers la comparaison d'échantillons d'ADN environnemental prélevés dans le sol de diverses parcelles forestières en Guyane française, via les outils classiques d'analyse statistique en écologie des communautés. Le deuxième chapitre se concentre sur les processus neutres d'assemblages des communautés.[...]
Integrative patterns of biodiversity, such as the distribution of taxa abundances and the spatial turnover of taxonomic composition, have been under scrutiny from ecologists for a long time, as they offer insight into the general rules governing the assembly of organisms into ecological communities. Thank to recent progress in high-throughput DNA sequencing, these patterns can now be measured in a fast and standardized fashion through the sequencing of DNA sampled from the environment (e.g. soil or water), instead of relying on tedious fieldwork and rare naturalist expertise. They can also be measured for the whole tree of life, including the vast and previously unexplored diversity of microorganisms. Taking full advantage of this new type of data is challenging however: DNA-based surveys are indirect, and suffer as such from many potential biases; they also produce large and complex datasets compared to classical censuses. The first goal of this thesis is to investigate how statistical tools and models classically used in ecology or coming from other fields can be adapted to DNA-based data so as to better understand the assembly of ecological communities. The second goal is to apply these approaches to soil DNA data from the Amazonian forest, the Earth's most diverse land ecosystem. Two broad types of mechanisms are classically invoked to explain the assembly of ecological communities: 'neutral' processes, i.e. the random birth, death and dispersal of organisms, and 'niche' processes, i.e. the interaction of the organisms with their environment and with each other according to their phenotype. Disentangling the relative importance of these two types of mechanisms in shaping taxonomic composition is a key ecological question, with many implications from estimating global diversity to conservation issues. In the first chapter, this question is addressed across the tree of life by applying the classical analytic tools of community ecology to soil DNA samples collected from various forest plots in French Guiana. The second chapter focuses on the neutral aspect of community assembly.[...]
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Kivinen, Jyri Juhani. "Statistical models for natural scene data". Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/8879.

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This thesis considers statistical modelling of natural image data. Obtaining advances in this field can have significant impact for both engineering applications, and for the understanding of the human visual system. Several recent advances in natural image modelling have been obtained with the use of unsupervised feature learning. We consider a class of such models, restricted Boltzmann machines (RBMs), used in many recent state-of-the-art image models. We develop extensions of these stochastic artificial neural networks, and use them as a basis for building more effective image models, and tools for computational vision. We first develop a novel framework for obtaining Boltzmann machines, in which the hidden unit activations co-transform with transformed input stimuli in a stable and predictable way throughout the network. We define such models to be transformation equivariant. Such properties have been shown useful for computer vision systems, and have been motivational for example in the development of steerable filters, a widely used classical feature extraction technique. Translation equivariant feature sharing has been the standard method for scaling image models beyond patch-sized data to large images. In our framework we extend shallow and deep models to account for other kinds of transformations as well, focusing on in-plane rotations. Motivated by the unsatisfactory results of current generative natural image models, we take a step back, and evaluate whether they are able to model a subclass of the data, natural image textures. This is a necessary subcomponent of any credible model for visual scenes. We assess the performance of a state- of-the-art model of natural images for texture generation, using a dataset and evaluation techniques from in prior work. We also perform a dissection of the model architecture, uncovering the properties important for good performance. Building on this, we develop structured extensions for more complicated data comprised of textures from multiple classes, using the single-texture model architecture as a basis. These models are shown to be able to produce state-of-the-art texture synthesis results quantitatively, and are also effective qualitatively. It is demonstrated empirically that the developed multiple-texture framework provides a means to generate images of differently textured regions, more generic globally varying textures, and can also be used for texture interpolation, where the approach is radically dfferent from the others in the area. Finally we consider visual boundary prediction from natural images. The work aims to improve understanding of Boltzmann machines in the generation of image segment boundaries, and to investigate deep neural network architectures for learning the boundary detection problem. The developed networks (which avoid several hand-crafted model and feature designs commonly used for the problem), produce the fastest reported inference times in the literature, combined with state-of-the-art performance.
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Steinberg, Daniel. "An Unsupervised Approach to Modelling Visual Data". Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9415.

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For very large visual datasets, producing expert ground-truth data for training supervised algorithms can represent a substantial human effort. In these situations there is scope for the use of unsupervised approaches that can model collections of images and automatically summarise their content. The primary motivation for this thesis comes from the problem of labelling large visual datasets of the seafloor obtained by an Autonomous Underwater Vehicle (AUV) for ecological analysis. It is expensive to label this data, as taxonomical experts for the specific region are required, whereas automatically generated summaries can be used to focus the efforts of experts, and inform decisions on additional sampling. The contributions in this thesis arise from modelling this visual data in entirely unsupervised ways to obtain comprehensive visual summaries. Firstly, popular unsupervised image feature learning approaches are adapted to work with large datasets and unsupervised clustering algorithms. Next, using Bayesian models the performance of rudimentary scene clustering is boosted by sharing clusters between multiple related datasets, such as regular photo albums or AUV surveys. These Bayesian scene clustering models are extended to simultaneously cluster sub-image segments to form unsupervised notions of “objects” within scenes. The frequency distribution of these objects within scenes is used as the scene descriptor for simultaneous scene clustering. Finally, this simultaneous clustering model is extended to make use of whole image descriptors, which encode rudimentary spatial information, as well as object frequency distributions to describe scenes. This is achieved by unifying the previously presented Bayesian clustering models, and in so doing rectifies some of their weaknesses and limitations. Hence, the final contribution of this thesis is a practical unsupervised algorithm for modelling images from the super-pixel to album levels, and is applicable to large datasets.
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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.

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Computer and Information Science
Ph.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
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LaMar, Michelle Marie. "Models for understanding student thinking using data from complex computerized science tasks". Thesis, University of California, Berkeley, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3686374.

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The Next Generation Science Standards (NGSS Lead States, 2013) define performance targets which will require assessment tasks that can integrate discipline knowledge and cross-cutting ideas with the practices of science. Complex computerized tasks will likely play a large role in assessing these standards, but many questions remain about how best to make use of such tasks within a psychometric framework (National Research Council, 2014). This dissertation explores the use of a more extensive cognitive modeling approach, driven by the extra information contained in action data collected while students interact with complex computerized tasks. Three separate papers are included. In Chapter 2, a mixture IRT model is presented that simultaneously classifies student understanding of a task while measuring student ability within their class. The model is based on differentially scoring the subtask action data from a complex performance. Simulation studies show that both class membership and class-specific ability can be reasonably estimated given sufficient numbers of items and response alternatives. The model is then applied to empirical data from a food-web task, providing some evidence of feasibility and validity. Chapter 3 explores the potential of using a more complex cognitive model for assessment purposes. Borrowing from the cognitive science domain, student decisions within a strategic task are modeled with a Markov decision process. Psychometric properties of the model are explored and simulation studies report on parameter recovery within the context of a simple strategy game. In Chapter 4 the Markov decision process (MDP) measurement model is then applied to an educational game to explore the practical benefits and difficulties of using such a model with real world data. Estimates from the MDP model are found to correlate more strongly with posttest results than a partial-credit IRT model based on outcome data alone.

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Maloo, Akshay. "Dynamic Behavior Visualizer: A Dynamic Visual Analytics Framework for Understanding Complex Networked Models". Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/25296.

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Dynamic Behavior Visualizer (DBV) is a visual analytics environment to visualize the spatial and temporal movements and behavioral changes of an individual or a group, e.g. family within a realistic urban environment. DBV is specifically designed to visualize the adaptive behavioral changes, as they pertain to the interactions with multiple inter-dependent infrastructures, in the aftermath of a large crisis, e.g. hurricane or the detonation of an improvised nuclear device. DBV is web-enabled and thus is easily accessible to any user with access to a web browser. A novel aspect of the system is its scale and fidelity. The goal of DBV is to synthesize information and derive insight from it; detect the expected and discover the unexpected; provide timely and easily understandable assessment and the ability to piece together all this information.
Master of Science
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Izumi, Kenji. "Application of Paleoenvironmental Data for Testing Climate Models and Understanding Past and Future Climate Variations". Thesis, University of Oregon, 2014. http://hdl.handle.net/1794/18510.

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Paleo data-model comparison is the process of comparing output from model simulations of past periods with paleoenvironmental data. It enables us to understand both the paleoclimate mechanism and responses of the earth environment to the climate and to evaluate how models work. This dissertation has two parts that each involve the development and application of approaches for data-model comparisons. In part 1, which is focused on the understanding of both past and future climatic changes/variations, I compare paleoclimate and historical simulations with future climate projections exploiting the fact that climate-model configurations are exactly the same in the paleo and future simulations in the Coupled Model Intercomparison Project Phase 5. In practice, I investigated large-scale temperature responses (land-ocean contrast, high-latitude amplification, and change in temperature seasonality) in paleo and future simulations, found broadly consistent relationships across the climate states, and validated the responses using modern observations and paleoclimate reconstructions. Furthermore, I examined the possibility that a small set of common mechanisms controls the large-scale temperature responses using a simple energy-balance model to decompose the temperature changes shown in warm and cold climate simulations and found that the clear-sky longwave downward radiation is a key control of the robust responses. In part 2, I applied the equilibrium terrestrial biosphere models, BIOME4 and BIOME5 (developed from BIOME4 herein), for reconstructing paleoclimate. I applied inverse modeling through the iterative forward-modeling (IMIFM) approach that uses the North American vegetation data to infer the mid-Holocene (MH, 6000 years ago) and the Last Glacial Maximum (LGM, 21,000 years ago) climates that control vegetation distributions. The IMIFM approach has the potential to provide more accurate quantitative climate estimates from pollen records than statistical approaches. Reconstructed North American MH and LGM climate anomaly patterns are coherent and consistent between variables and between BIOME4 and BIOME5, and these patterns are also consistent with previous data synthesis. This dissertation includes previously published and unpublished coauthored material.
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Lipecki, Johan, e Viggo Lundén. "The Effect of Data Quantity on Dialog System Input Classification Models". Thesis, KTH, Hälsoinformatik och logistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-237282.

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This paper researches how different amounts of data affect different word vector models for classification of dialog system user input. A hypothesis is tested that there is a data threshold for dense vector models to reach the state-of-the-art performance that have been shown with recent research, and that character-level n-gram word-vector classifiers are especially suited for Swedish classifiers–because of compounding and the character-level n-gram model ability to vectorize out-of-vocabulary words. Also, a second hypothesis is put forward that models trained with single statements are more suitable for chat user input classification than models trained with full conversations. The results are not able to support neither of our hypotheses but show that sparse vector models perform very well on the binary classification tasks used. Further, the results show that 799,544 words of data is insufficient for training dense vector models but that training the models with full conversations is sufficient for single statement classification as the single-statement- trained models do not show any improvement in classifying single statements.
Detta arbete undersöker hur olika datamängder påverkar olika slags ordvektormodeller för klassificering av indata till dialogsystem. Hypotesen att det finns ett tröskelvärde för träningsdatamängden där täta ordvektormodeller när den högsta moderna utvecklingsnivån samt att n-gram-ordvektor-klassificerare med bokstavs-noggrannhet lämpar sig särskilt väl för svenska klassificerare söks bevisas med stöd i att sammansättningar är särskilt produktiva i svenskan och att bokstavs-noggrannhet i modellerna gör att tidigare osedda ord kan klassificeras. Dessutom utvärderas hypotesen att klassificerare som tränas med enkla påståenden är bättre lämpade att klassificera indata i chattkonversationer än klassificerare som tränats med hela chattkonversationer. Resultaten stödjer ingendera hypotes utan visar istället att glesa vektormodeller presterar väldigt väl i de genomförda klassificeringstesterna. Utöver detta visar resultaten att datamängden 799 544 ord inte räcker till för att träna täta ordvektormodeller väl men att konversationer räcker gott och väl för att träna modeller för klassificering av frågor och påståenden i chattkonversationer, detta eftersom de modeller som tränats med användarindata, påstående för påstående, snarare än hela chattkonversationer, inte resulterar i bättre klassificerare för chattpåståenden.
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Abufouda, Mohammed [Verfasser], e Katharina [Akademischer Betreuer] Zweig. "Learning From Networked-data: Methods and Models for Understanding Online Social Networks Dynamics / Mohammed Abufouda ; Betreuer: Katharina Zweig". Kaiserslautern : Technische Universität Kaiserslautern, 2020. http://d-nb.info/1221599747/34.

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Wojatzki, Michael Maximilian [Verfasser], e Torsten [Akademischer Betreuer] Zesch. "Computer-assisted understanding of stance in social media : formalizations, data creation, and prediction models / Michael Maximilian Wojatzki ; Betreuer: Torsten Zesch". Duisburg, 2019. http://d-nb.info/1177681471/34.

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Livros sobre o assunto "Understanding of data models"

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Ashwin, Ram, e Moorman Kenneth, eds. Understanding language understanding: Computational models of reading. Cambridge, Mass: MIT Press, 1999.

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Klüver, Jürgen. Social Understanding: On Hermeneutics, Geometrical Models and Artificial Intelligence. Dordrecht: Springer Science+Business Media B.V., 2011.

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3

Bakeman, Roger. Understanding log-linear analysis with ILOG: An interactive approach. Hillsdale, N.J: L. Erlbaum, 1994.

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Berry, Joseph K. Map analysis: Understanding spatial patterns and relationships. San Francisco, CA: GeoTec Media, 2007.

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Meju, Max A. Geophysical data analysis: Understanding inverse problem theory and practice. Tulsa, OK: Society of Exploration Geophysicists, 1994.

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Alvarado, Sergio Jose. Understanding editorial text: A computer model of argument comprehension. Boston: Kluwer Academic Publishers, 1990.

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1941-, Taylor Arlene G., ed. Understanding FRBR: What it is and how it will affect our retrieval tools. Westport, Conn: Libraries Unlimited, 2007.

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Erickson, Bonnie H. Understanding data. 2a ed. Toronto: University of Toronto Press, 1992.

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Erickson, Bonnie H. Understanding data. 2a ed. Buckingham: Open University Press, 1992.

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Kauffels, Franz-Joachim. Understanding data communications. Chichester, West Sussex, England: Ellis Horwood, 1989.

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Capítulos de livros sobre o assunto "Understanding of data models"

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Westfall, Peter H., e Andrea L. Arias. "Censored Data Models". In Understanding Regression Analysis, 379–403. Boca Raton : CRC Press, [2020]: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781003025764-15.

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David, Salsburg. "5 Models Versus Data". In Understanding Randomness, 85–96. CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742: CRC Press, 2017. http://dx.doi.org/10.1201/9780203734674-7.

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Holloway, Paul. "Spatial Data Models". In Understanding GIS through Sustainable Development Goals, 21–42. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003220510-5.

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Matthews, David Edward, e Vernon Todd Farewell. "12 Regression Models for Count Data". In Using and Understanding Medical Statistics, 141–48. Basel: KARGER, 2007. http://dx.doi.org/10.1159/000099427.

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Badiru, Adedeji B. "Data Analytics Tools for Understanding Random Field Regression Models *". In Data Analytics, 211–32. First edition. | Boca Raton, FL : CRC Press/Taylor & Francis: CRC Press, 2020. http://dx.doi.org/10.1201/9781003083146-7.

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Hand, David J. "Intelligent Data Analysis and Deep Understanding". In Causal Models and Intelligent Data Management, 67–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58648-4_5.

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Jung, Dominik. "Business Data Understanding". In The Modern Business Data Analyst, 49–110. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59907-1_3.

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Matthews, David Edward, e Vernon Todd Farewell. "10 Linear Regression Models for Medical Data". In Using and Understanding Medical Statistics, 111–27. Basel: KARGER, 2007. http://dx.doi.org/10.1159/000099425.

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Liiv, Innar. "Understanding the Data Model". In Behaviormetrics: Quantitative Approaches to Human Behavior, 1–13. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2418-6_1.

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Walloth, Christian, Ernst Gebetsroither-Geringer e Funda Atun. "Introduction: Overcoming Limitations of Urban Systems Models and of Data Availability". In Understanding Complex Systems, 1–14. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30178-5_1.

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Trabalhos de conferências sobre o assunto "Understanding of data models"

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Honavar, Vasant. "Learning predictive models from large distributed autonomous data sources". In 2012 Conference on Intelligent Data Understanding (CIDU). IEEE, 2012. http://dx.doi.org/10.1109/cidu.2012.6382178.

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Holland, Marika. "Investigation of the climate system using Earth System models". In 2012 Conference on Intelligent Data Understanding (CIDU). IEEE, 2012. http://dx.doi.org/10.1109/cidu.2012.6382181.

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Gorinevsky, Dimitry, Bryan Matthews e Rodney Martin. "Aircraft anomaly detection using performance models trained on fleet data". In 2012 Conference on Intelligent Data Understanding (CIDU). IEEE, 2012. http://dx.doi.org/10.1109/cidu.2012.6382196.

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Satkin, Scott, Jason Lin e Martial Hebert. "Data-Driven Scene Understanding from 3D Models". In British Machine Vision Conference 2012. British Machine Vision Association, 2012. http://dx.doi.org/10.5244/c.26.128.

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Cui, Jia, Yonggang Deng e Bowen Zhou. "Reinforcing language model for speech translation with auxiliary data". In Understanding (ASRU). IEEE, 2009. http://dx.doi.org/10.1109/asru.2009.5373308.

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Pek, Yun Ning, e Kwan Hui Lim. "Identifying and Understanding Business Trends using Topic Models with Word Embedding". In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9005497.

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Katsumaru, Masaki, Mikio Nakano, Kazunori Komatani, Kotaro Funakoshi, Tetsuya Ogata e Hiroshi G. Okuno. "Improving speech understanding accuracy with limited training data using multiple language models and multiple understanding models". In Interspeech 2009. ISCA: ISCA, 2009. http://dx.doi.org/10.21437/interspeech.2009-699.

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Shih-Hung Liu, Fang-Hui Chu, Shih-Hsiang Lin, Hung-Shin Lee e Berlin Chen. "Training data selection for improving discriminative training of acoustic models". In 2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU). IEEE, 2007. http://dx.doi.org/10.1109/asru.2007.4430125.

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Hoernle, Nicholas, Kobi Gal, Barbara Grosz, Leilah Lyons, Ada Ren e Andee Rubin. "Interpretable Models for Understanding Immersive Simulations". In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/321.

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This paper describes methods for comparative evaluation of the interpretability of models of high dimensional time series data inferred by unsupervised machine learning algorithms. The time series data used in this investigation were logs from an immersive simulation like those commonly used in education and healthcare training. The structures learnt by the models provide representations of participants' activities in the simulation which are intended to be meaningful to people's interpretation. To choose the model that induces the best representation, we designed two interpretability tests, each of which evaluates the extent to which a model’s output aligns with people’s expectations or intuitions of what has occurred in the simulation. We compared the performance of the models on these interpretability tests to their performance on statistical information criteria. We show that the models that optimize interpretability quality differ from those that optimize (statistical) information theoretic criteria. Furthermore, we found that a model using a fully Bayesian approach performed well on both the statistical and human-interpretability measures. The Bayesian approach is a good candidate for fully automated model selection, i.e., when direct empirical investigations of interpretability are costly or infeasible.
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Jin, Ruoming, Dong Li, Jing Gao, Zhi Liu, Li Chen e Yang Zhou. "Towards a Better Understanding of Linear Models for Recommendation". In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3447548.3467428.

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Relatórios de organizações sobre o assunto "Understanding of data models"

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Glass, Samuel V., Samuel L. Zelinka, Charles R. Boardman e Emil Engelund Thybring. Promoting advances in understanding water vapor sorption in wood: relegating popular models and misconceptions. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541615744.

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Water vapor sorption is a fundamental characteristic of wood as a building material. Apart from empirical prediction, models are often used to interpret the time-dependent process of water vapor uptake (sorption kinetics) and equilibrium states of water in wood (sorption isotherms). This paper summarizes our recent investigations into measurement methods and popular models that are widely used for interpreting these physical phenomena. Commonly used criteria for determining equilibrium moisture content with the dynamic vapor sorption technique yield much larger errors than previously thought. A more rigorous equilibrium criterion and a method to reduce data acquisition time are proposed. Evaluation of the parallel exponential kinetics model with improved data and multi-exponential decay analysis indicates that this model is unable to characterize the full sorption kinetic response following a step change in relative humidity. Fitting of common sorption isotherm models to high-quality equilibrium data for wood gives model predicted physical quantities such as monolayer capacity and enthalpy of sorption that are far from agreement with independently measured data. Thus, these models are not valid for water vapor sorption in wood. New theoretical models are needed that correctly describe the physical phenomena.
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Weijters, Bert. Analyzing Experimental Data in Structural Equation Models. Instats Inc., 2023. http://dx.doi.org/10.61700/zclk0a8vgkfaa706.

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This two-day workshop, 'Analyzing Experimental Data using Structural Equation Modeling', led by Bert Weijters from Ghent University, provides a comprehensive understanding of SEM and its applications in research, with a focus on using Mplus software for SEM analysis. Ideal for PhD students, professors, and professional researchers in Psychology, Education, Management, and Marketing, the seminar offers practical experience in data analysis and an official Instats certificate of completion, with ECTS Equivalent points for European students.
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Gruber, Peter. Using ChatGPT for Advanced Data Analysis. Instats Inc., 2023. http://dx.doi.org/10.61700/pmgm4wmm7ffer469.

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This workshop teaches researchers, from PhD students to professors, how to use ChatGPT and its Advanced Data Analytics tool for statistical analysis without writing a line of code or even knowing how to use a statistics program. The seminar covers a range of topics from data preparation and descriptive statistics to regression analysis, advanced statistical tests and visualisation. Special emphasis is placed on understanding the workings and limits of AI models such as ChatGPT and reflecting on its implications for data analysis.
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Gruber, Peter H. Using ChatGPT for Advanced Data Analysis. Instats Inc., 2023. http://dx.doi.org/10.61700/zqir2dzchct5b469.

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This 2-day workshop teaches researchers, from PhD students to professors, how to use ChatGPT and its Advanced Data Analytics tool for statistical analysis without writing a line of code or even knowing how to use a statistics program. The seminar covers a range of topics from data preparation and descriptive statistics to regression analysis, advanced statistical tests and visualisation. Special emphasis is placed on understanding the workings and limits of AI models such as ChatGPT and reflecting on its implications for data analysis.
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Gruber, Peter. Using ChatGPT for Advanced Data Analysis 2.0. Instats Inc., 2023. http://dx.doi.org/10.61700/txvjolg6id2hj469.

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This 2-day workshop (specifically tailored for the Australian/Asian and US West coast time zones) is aimed at non-statisticians who have to perform data analysis in their work or research. It teaches how to use ChatGPT for statistical analysis without having to know a statistics program or writing a single line of code. The seminar covers a range of topics from data preparation and descriptive statistics to regression analysis, advanced statistical tests and visualisation. Special emphasis is placed on understanding the workings and limits of AI models such as ChatGPT and reflecting on its implications for data analysis.
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Grimm, Kevin. Factor Analysis and Measurement Invariance with Categorical Data in Mplus. Instats Inc., 2024. http://dx.doi.org/10.61700/2c14h0c6ktix9661.

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The 'Factor Analysis and Measurement Invariance with Categorical Data in Mplus' seminar, led by professor Kevin Grimm from Arizona State University, offers a comprehensive understanding of confirmatory factor models for binary and ordered categorical indicators and the examination of measurement invariance, a critical concept in social sciences research. Participants will gain hands-on experience in using Mplus for analysis, learn to interpret and report results effectively, and acquire strategies to deal with non-invariance, enhancing their research skills and ensuring the robustness of their research.
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Grimm, Kevin. Factor Analysis and Measurement Invariance with Categorical Data in R. Instats Inc., 2024. http://dx.doi.org/10.61700/6q6pcruvzduci667.

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The 'Factor Analysis and Measurement Invariance with Categorical Data in R' seminar, led by professor Kevin Grimm from Arizona State University, offers a comprehensive understanding of confirmatory factor models for binary and ordered categorical indicators and the examination of measurement invariance, a critical concept in social sciences research. Participants will gain hands-on experience in using the lavaan package in R for analysis, learn to interpret and report results effectively, and acquire strategies to deal with non-invariance, enhancing their research skills and ensuring the robustness of their research.
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de Padua, David, Matteo Lanzafame, Irfan Qureshi e Kiyoshi Taniguchi. Understanding the Drivers of Remittances to Pakistan. Asian Development Bank, julho de 2024. http://dx.doi.org/10.22617/wps240348-2.

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This paper analyzes remittances to Pakistan and identifies the key macroeconomic variables influencing this important source of external financing. Remittances account for approximately 10% of gross domestic product in Pakistan and a better understanding of remittance drivers is needed to inform policies that can bolster their contribution to poverty reduction and other development priorities. To develop this understanding, the authors combined a database of bilateral remittances between Pakistan and its main remittance-sending countries with monthly macroeconomic data over 2003–2021, and applied a Bayesian vector autoregression model to assess the drivers. The macroeconomic variables identified included economic activity, inflation, equity markets, and interest rates— both in Pakistan and migrants’ host countries—and all play a significant role, although their contributions vary over time.
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Altman, Safra, Krystyna Powell e Marin Kress. Marine bioinvasion risk : review of current ecological models. Engineer Research and Development Center (U.S.), outubro de 2023. http://dx.doi.org/10.21079/11681/47820.

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This special report describes the first phase of developing an ecological model to inform marine bioinvasion risks in the United States. The project responds to the needs of the US Army Corps of Engineers (USACE) Aquatic Nuisance Species Research Program, or ANSRP, which addresses all problematic invasive aquatic species affecting the nation’s waterways, infrastructure, and associated resources, and the needs of the USACE navigation and dredging programs. Multiple port-deepening studies are either in progress or under consideration, and all must address ecological risk. Understanding whether and how increased dredging contributes to increased marine bioinvasion risk allows risk mitigation during early planning phases. Considering the potential impacts of future environmental change, such as changing sea level, ocean temperature, and ocean chemistry, will further strengthen planning for marine bioinvasion risk. Therefore, this special report documents current ecological modeling approaches to marine bioinvasion risk models and identifies models that incorporate shipping as a vector. The special report then presents a conceptual model and identifies historic vessel position data from the Automatic Identification System, or AIS, now available for most commercial and some recreational vessels around the United States, as a key source for future model development and testing.
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Lieng, Sotberg e Brennodden. L51570 Energy Based Pipe-Soil Interaction Models. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), junho de 1988. http://dx.doi.org/10.55274/r0010091.

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The purpose of this project was to complete a handbook with practical design procedures for submarine pipeline on-bottom stability. The remaining part of the handbook was primarily a description of the interaction between non-trenched pipelines and the seabed where the pipelines were free to move under environmental loading. The objective of this project was to determine the lateral soil resistance forces on a pipeline moving cyclically during hydro-dynamic loading. To meet the goal, full-scale pipe-soil interaction tests were conducted. The models presented in this report are based on the results and general understanding obtained from 110 experimental tests of pipe-soil interaction on loose and dense sand, and soft clay. Raw data from 29 experimental tests on stiff clay in the PIPESTAB project have been qualitatively considered.
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