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Relevant bibliographies by topics / Sampling

Academic literature on the topic 'Sampling'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 August 2024

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Dissertations / Theses
Books
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Journal articles on the topic "Sampling"

1

Dumas, Pascal, and Gilles Fontanini. "Sampling fauna in aquifers: a comparison of net-sampling and pumping." Fundamental and Applied Limnology 150, no. 4 (February 15, 2001): 661–76. http://dx.doi.org/10.1127/archiv-hydrobiol/150/2001/661.

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Guochang, Wu, Zhang Yadong, and Yang Xiaohui. "Sampling Theory: From Shannon Sampling Theorem to Compressing Sampling." Information Technology Journal 9, no. 6 (August 1, 2010): 1231–35. http://dx.doi.org/10.3923/itj.2010.1231.1235.

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TANAKA, Isamu, and Takashi AKIYAMA. "A Personal Sampling and an Area Sampling." Journal of UOEH 7, no. 2 (1985): 207–11. http://dx.doi.org/10.7888/juoeh.7.207.

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Pinto, J. Sousa, and R. F. Hoskins. "Sampling and Π-sampling expansions." Proceedings Mathematical Sciences 110, no. 4 (November 2000): 379–92. http://dx.doi.org/10.1007/bf02829533.

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Emerson, Robert Wall. "Convenience Sampling, Random Sampling, and Snowball Sampling: How Does Sampling Affect the Validity of Research?" Journal of Visual Impairment & Blindness 109, no. 2 (March 2015): 164–68. http://dx.doi.org/10.1177/0145482x1510900215.

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Egger, Joseph. "POP-analysis and sampling interval." Meteorologische Zeitschrift 10, no. 4 (October 15, 2001): 351–55. http://dx.doi.org/10.1127/0941-2948/2001/0010-0351.

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Sen, A. R., and Steven K. Thompson. "Sampling." Journal of the American Statistical Association 88, no. 424 (December 1993): 1471. http://dx.doi.org/10.2307/2291302.

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Cormack, R. M., and S. K. Thompson. "Sampling." Biometrics 49, no. 4 (December 1993): 1283. http://dx.doi.org/10.2307/2532279.

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Ziegel, Eric R. "Sampling." Technometrics 44, no. 4 (November 2002): 407. http://dx.doi.org/10.1198/tech.2002.s93.

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Campbell, Malcolm. "Sampling." African Journal of Midwifery and Women's Health 10, no. 1 (January 2, 2016): 9–13. http://dx.doi.org/10.12968/ajmw.2016.10.1.9.

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Dissertations / Theses on the topic "Sampling"

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Malik, M. B. "Acceptance sampling : Robust alternatives for sampling by variables." Thesis, University of Essex, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.482618.

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Hörmann, Wolfgang, and Josef Leydold. "Monte Carlo Integration Using Importance Sampling and Gibbs Sampling." Department of Statistics and Mathematics, Abt. f. Angewandte Statistik u. Datenverarbeitung, WU Vienna University of Economics and Business, 2005. http://epub.wu.ac.at/1642/1/document.pdf.

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To evaluate the expectation of a simple function with respect to a complicated multivariate density Monte Carlo integration has become the main technique. Gibbs sampling and importance sampling are the most popular methods for this task. In this contribution we propose a new simple general purpose importance sampling procedure. In a simulation study we compare the performance of this method with the performance of Gibbs sampling and of importance sampling using a vector of independent variates. It turns out that the new procedure is much better than independent importance sampling; up to dimension five it is also better than Gibbs sampling. The simulation results indicate that for higher dimensions Gibbs sampling is superior. (author's abstract)
Series: Preprint Series / Department of Applied Statistics and Data Processing

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Meister, Kadri. "On Methods for Real Time Sampling and Distributions in Sampling." Doctoral thesis, Umeå : Univ, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-415.

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Miljak, Marija. "Conformational sampling of intrinsically disordered peptides by enhanced sampling methods." Thesis, University of Southampton, 2017. https://eprints.soton.ac.uk/422232/.

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The aim of this study was to explore the conformational equilibrium of four cyclic hormone peptides in order to investigate to what extent the bound conformational state can be observed from the solution phase simulations. The studied cyclic peptides share the same structural motif of a six membered ring closed by disulphide bridge between the cysteine residues. They also belong to the class of intrinsically disordered peptides known to exist in an equilibrium of different conformations. Elucidating their conformational ensemble using traditional experimental techniques has proven hard due to the fast interconversion between conformational states, and thus molecular dynamics simulation may help in providing a detailed picture of the peptide’s conformational ensemble. However, conventional molecular dynamics simulation are limited by the long time scale required to observe many conformational motions. Therefore in this work Replica Exchange techniques were applied to test the rate of convergence in conformational sampling. Moreover, to predict the conformational equilibrium of the peptides, a combination of results from enhanced sampling methods, DFT calculations and NMR experiments was used. It was found that calculated chemical shifts weighted by the ensemble populations of each conformational state were better able to reproduce the experimental chemical shift data, over and above any single peptide conformation. This result supports the use of enhanced sampling molecular dynamics computer simulations to study intrinsically disordered peptides. The knowledge of the conformational equilibrium and the relative populations of the unbound states of the peptides obtained using this approach may help in predicting the structural and functional roles of the bound state peptide. Another purpose of this work was also to check the extent to which a difference in peptide sequence may contribute to their functional diversity. Finally, the performance of the Replica Exchange simulations was compared, indicating that Solute Tempering is to be preferred over temperature Replica Exchange for reasons of computational efficiency.

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Grepstad, Sigrid. "Sampling on Quasicrystals." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for matematiske fag, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-12650.

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We prove that quasicrystals are universal sets of stable sampling in any dimension. Necessary and sufficient density conditions for stable sampling and interpolation sets in one dimension are studied in detail.

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Hörmann, Wolfgang, and Josef Leydold. "Quasi Importance Sampling." Department of Statistics and Mathematics, Abt. f. Angewandte Statistik u. Datenverarbeitung, WU Vienna University of Economics and Business, 2005. http://epub.wu.ac.at/1394/1/document.pdf.

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There arise two problems when the expectation of some function with respect to a nonuniform multivariate distribution has to be computed by (quasi-) Monte Carlo integration: the integrand can have singularities when the domain of the distribution is unbounded and it can be very expensive or even impossible to sample points from a general multivariate distribution. We show that importance sampling is a simple method to overcome both problems. (author's abstract)
Series: Preprint Series / Department of Applied Statistics and Data Processing

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Gatarić, Milana. "Nonuniform generalized sampling." Thesis, University of Cambridge, 2016. https://www.repository.cam.ac.uk/handle/1810/254971.

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In this thesis we study a novel approach to stable recovery of unknown compactly supported L2 functions from finitely many nonuniform samples of their Fourier transform, so-called Nonuniform Generalized Sampling (NUGS). This framework is based on a recently introduced idea of generalized sampling for stable sampling and reconstruction in abstract Hilbert spaces, which allows one to tailor the reconstruction space to suit the function to be approximated and thereby obtain rapidly-convergent approximations. While preserving this important hallmark, NUGS describes sampling by the use of weighted Fourier frames, thus allowing for highly nonuniform sampling schemes with the points taken arbitrarily close. The particular setting of NUGS directly corresponds to various image recovery models ubiquitous in applications such as magnetic resonance imaging, computed tomography and electron microscopy, where Fourier samples are often taken not necessarily on a Cartesian grid, but rather along spiral trajectories or radial lines. Specifically, NUGS provides stable recovery in a desired reconstruction space subject to sufficient sampling density and sufficient sampling bandwidth, where the latter depends solely on the particular reconstruction space. For univariate compactly supported wavelets, we show that only a linear scaling between the number of wavelets and the sampling bandwidth is both sufficient and necessary for stable recovery. Furthermore, in the wavelet case, we provide an efficient implementation of NUGS for recovery of wavelet coefficients from Fourier data. Additionally, the sufficient relation between the dimension of the reconstruction space and the bandwidth of the nonuniform samples is analysed for the reconstruction spaces of piecewise polynomials or splines with a nonequidistant sequence of knots, and it is shown that this relation is also linear for splines and piecewise polynomials of fixed degree, but quadratic for piecewise polynomials of varying degree. In order to derive explicit guarantees for stable recovery from nonuniform samples in terms of the sampling density, we also study conditions sufficient to ensure existence of a particular frame. Firstly, we establish the sharp and dimensionless sampling density that is sufficient to guarantee a weighted Fourier frame for the space of multivariate compactly supported L2 functions. Furthermore, subject to non-sharp densities, we improve existing estimates of the corresponding frame bounds. Secondly, we provide sampling densities sufficient to ensure a frame, as well as, estimates of the corresponding frame bounds, when a multivariate bandlimited function and its derivatives are sampled at nonuniformly spaced points.

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Fike, William H. "Lobster Sampling Trap." Fogler Library, University of Maine, 2007. http://www.library.umaine.edu/theses/pdf/FikeWH2007.pdf.

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Fang, Jing. "Herded Gibbs sampling." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/43188.

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The Gibbs sampler is one of the most popular algorithms for inference in statistical models. In this thesis, we introduce a herding variant of this algorithm that is entirely deterministic. We demonstrate, with simple examples, that herded Gibbs exhibits better convergence behavior for approximating the marginal distributions than Gibbs sampling. In particular, image denoising exemplifies the effectiveness of herded Gibbs as an inference technique for Markov Random Fields (MRFs). Also, we adopt herded Gibbs as the inference engine for Conditional Random Fields (CRFs) in Named Entity Recognition (NER) and show that it is competitive with the state of the art. The conclusion is that herded Gibbs, for graphical models with nodes of low degree, is very close to Gibbs sampling in terms of the complexity of the code and computation, but that it converges much faster.

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Raja, Yogesh. "Adaptive visual sampling." Thesis, Queen Mary, University of London, 2010. http://qmro.qmul.ac.uk/xmlui/handle/123456789/607.

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Various visual tasks may be analysed in the context of sampling from the visual field. In visual psychophysics, human visual sampling strategies have often been shown at a high-level to be driven by various information and resource related factors such as the limited capacity of the human cognitive system, the quality of information gathered, its relevance in context and the associated efficiency of recovering it. At a lower-level, we interpret many computer vision tasks to be rooted in similar notions of contextually-relevant, dynamic sampling strategies which are geared towards the filtering of pixel samples to perform reliable object association. In the context of object tracking, the reliability of such endeavours is fundamentally rooted in the continuing relevance of object models used for such filtering, a requirement complicated by realworld conditions such as dynamic lighting that inconveniently and frequently cause their rapid obsolescence. In the context of recognition, performance can be hindered by the lack of learned context-dependent strategies that satisfactorily filter out samples that are irrelevant or blunt the potency of models used for discrimination. In this thesis we interpret the problems of visual tracking and recognition in terms of dynamic spatial and featural sampling strategies and, in this vein, present three frameworks that build on previous methods to provide a more flexible and effective approach. Firstly, we propose an adaptive spatial sampling strategy framework to maintain statistical object models for real-time robust tracking under changing lighting conditions. We employ colour features in experiments to demonstrate its effectiveness. The framework consists of five parts: (a) Gaussian mixture models for semi-parametric modelling of the colour distributions of multicolour objects; (b) a constructive algorithm that uses cross-validation for automatically determining the number of components for a Gaussian mixture given a sample set of object colours; (c) a sampling strategy for performing fast tracking using colour models; (d) a Bayesian formulation enabling models of object and the environment to be employed together in filtering samples by discrimination; and (e) a selectively-adaptive mechanism to enable colour models to cope with changing conditions and permit more robust tracking. Secondly, we extend the concept to an adaptive spatial and featural sampling strategy to deal with very difficult conditions such as small target objects in cluttered environments undergoing severe lighting fluctuations and extreme occlusions. This builds on previous work on dynamic feature selection during tracking by reducing redundancy in features selected at each stage as well as more naturally balancing short-term and long-term evidence, the latter to facilitate model rigidity under sharp, temporary changes such as occlusion whilst permitting model flexibility under slower, long-term changes such as varying lighting conditions. This framework consists of two parts: (a) Attribute-based Feature Ranking (AFR) which combines two attribute measures; discriminability and independence to other features; and (b) Multiple Selectively-adaptive Feature Models (MSFM) which involves maintaining a dynamic feature reference of target object appearance. We call this framework Adaptive Multi-feature Association (AMA). Finally, we present an adaptive spatial and featural sampling strategy that extends established Local Binary Pattern (LBP) methods and overcomes many severe limitations of the traditional approach such as limited spatial support, restricted sample sets and ad hoc joint and disjoint statistical distributions that may fail to capture important structure. Our framework enables more compact, descriptive LBP type models to be constructed which may be employed in conjunction with many existing LBP techniques to improve their performance without modification. The framework consists of two parts: (a) a new LBP-type model known as Multiscale Selected Local Binary Features (MSLBF); and (b) a novel binary feature selection algorithm called Binary Histogram Intersection Minimisation (BHIM) which is shown to be more powerful than established methods used for binary feature selection such as Conditional Mutual Information Maximisation (CMIM) and AdaBoost.

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Books on the topic "Sampling"

1

Fox, Nicholas J. Sampling. [s.l.]: NHS Executive, Trent, 1998.

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Thompson, Steven K. Sampling. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118162934.

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Lawrimore, Kay, and Hari Rajagopalan. Sampling. 2455 Teller Road, Thousand Oaks California 91320 United States: SAGE Publications, Inc., 2023. http://dx.doi.org/10.4135/9781071910177.

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R, Krishnaiah Paruchuri, and Rao C. Radhakrishna 1920-, eds. Sampling. Amsterdam: North-Holland, 1988.

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Thompson, Steven K. Sampling. New York: Wiley, 1992.

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Pitard, Francis F. Theory of Sampling and Sampling Practice. Boca Raton, Florida, USA: Chapman and Hall/CRC, 2019.

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Patil, Ganapati P., Sharad D. Gore, and Charles Taillie. Composite Sampling. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-7628-4.

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Freeden, Willi, M. Zuhair Nashed, and Michael Schreiner. Spherical Sampling. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-71458-5.

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Buckland, S. T., D. R. Anderson, K. P. Burnham, and J. L. Laake. Distance Sampling. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1572-8.

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Buckland, S. T., D. R. Anderson, K. P. Burnham, and J. L. Laake. Distance Sampling. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1574-2.

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Book chapters on the topic "Sampling"

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Lohr, Sharon L. "Two-Phase Sampling." In Sampling, 457–82. 3rd ed. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429298899-12.

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Lohr, Sharon L. "Categorical Data Analysis in Complex Surveys." In Sampling, 395–418. 3rd ed. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429298899-10.

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Lohr, Sharon L. "Nonresponse." In Sampling, 311–58. 3rd ed. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429298899-8.

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Lohr, Sharon L. "Variance Estimation in Complex Surveys." In Sampling, 359–94. 3rd ed. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429298899-9.

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Lohr, Sharon L. "Cluster Sampling with Equal Probabilities." In Sampling, 167–218. 3rd ed. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429298899-5.

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Lohr, Sharon L. "Rare Populations and Small Area Estimation." In Sampling, 499–516. 3rd ed. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429298899-14.

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Lohr, Sharon L. "Stratified Sampling." In Sampling, 79–120. 3rd ed. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429298899-3.

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Lohr, Sharon L. "Sampling with Unequal Probabilities." In Sampling, 219–72. 3rd ed. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429298899-6.

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Lohr, Sharon L. "Complex Surveys." In Sampling, 273–310. 3rd ed. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429298899-7.

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Lohr, Sharon L. "Nonprobability Samples." In Sampling, 517–56. 3rd ed. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429298899-15.

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Conference papers on the topic "Sampling"

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Syrjala, Ville, Vesa Lehtinen, and Mikko Valkama. "Sampling jitter in charge sampling radio." In 2012 IEEE Wireless Communications and Networking Conference Workshops. IEEE, 2012. http://dx.doi.org/10.1109/wcncw.2012.6215541.

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Masirevic, Dragana Jankov, Tibor K. Pogany, Arpad Bariez, and Aurel Galantai. "Sampling bessel functions and bessel sampling." In 2013 IEEE 8th International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, 2013. http://dx.doi.org/10.1109/saci.2013.6608942.

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Blanchet, Jose H., and Jingchen Liu. "Path-sampling for state-dependent importance sampling." In 2007 Winter Simulation Conference. IEEE, 2007. http://dx.doi.org/10.1109/wsc.2007.4419626.

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Syrjala, Ville, and Mikko Valkama. "Sampling Jitter Cancellation in Direct-Sampling Radio." In Networking Conference (WCNC). IEEE, 2010. http://dx.doi.org/10.1109/wcnc.2010.5506638.

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Yu, Hongbin. "Least Sampling Density from High Order Sampling." In 2010 2nd International Workshop on Intelligent Systems and Applications (ISA). IEEE, 2010. http://dx.doi.org/10.1109/iwisa.2010.5473455.

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Zancato, Luca, and Alessandro Chiuso. "Sampling matters: SGD smoothing through importance sampling." In 2022 IEEE 61st Conference on Decision and Control (CDC). IEEE, 2022. http://dx.doi.org/10.1109/cdc51059.2022.9992486.

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Smidova, Magdalena, and Miroslav Vorechovsky. "Comparison of Sampling Schemes in Asymptotic Sampling." In 3rd International Symposium on Uncertainty Quantification and Stochastic Modeling. Rio de Janeiro, Brazil: ABCM Brazilian Society of Mechanical Sciences and Engineering, 2015. http://dx.doi.org/10.20906/cps/usm-2016-0071.

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Chai, Jin-Xiang, Shing-Chow Chan, Heung-Yeung Shum, and Xin Tong. "Plenoptic sampling." In the 27th annual conference. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/344779.344932.

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Del Pero, Luca, Jinyan Guan, Ernesto Brau, Joseph Schlecht, and Kobus Barnard. "Sampling bedrooms." In 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2011. http://dx.doi.org/10.1109/cvpr.2011.5995737.

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Bogliolo, Alessandro, and Luca Benini. "Node sampling." In the 1998 IEEE/ACM international conference. New York, New York, USA: ACM Press, 1998. http://dx.doi.org/10.1145/288548.289071.

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Reports on the topic "Sampling"

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Corriveau, Elizabeth, and Jay Clausen. Application of Incremental Sampling Methodology for subsurface sampling. Engineer Research and Development Center (U.S.), May 2021. http://dx.doi.org/10.21079/11681/40480.

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Historically, researchers studying contaminated sites have used grab sampling to collect soil samples. However, this methodology can introduce error in the analysis because it does not account for the wide variations of contaminant concentrations in soil. An alternative method is the Incremental Sampling Methodology (ISM), which previous studies have shown more accurately captures the true concentration of contaminants over an area, even in heterogeneous soils. This report describes the methods and materials used with ISM to collect soil samples, specifically for the purpose of mapping subsurface contamination from site activities. The field data presented indicates that ISM is a promising methodology for collecting subsurface soil samples containing contaminants of concern, including metals and semivolatile organic compounds (SVOCs), for analysis. Ultimately, this study found ISM to be useful for supplying information to assist in the decisions needed for remediation activities.

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Verver, S. W. Factsheet: Sampling pelagic fisheries through self-sampling (PEL2). Centrum voor Visserijonderzoek (CVO), 2023. http://dx.doi.org/10.18174/634043.

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Olander, A. R. ,. Westinghouse Hanford. 340 Representative sampling verification tank sampling and analysis plan. Office of Scientific and Technical Information (OSTI), August 1996. http://dx.doi.org/10.2172/657431.

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Olander, A. R. ,. Westinghouse Hanford. 340 Representative sampling verification tank sampling and analysis plan. Office of Scientific and Technical Information (OSTI), August 1996. http://dx.doi.org/10.2172/657707.

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Halgren, D. L. ,. Westinghouse Hanford. 340 representative sampling verification tank sampling and analysis plan. Office of Scientific and Technical Information (OSTI), September 1996. http://dx.doi.org/10.2172/657838.

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van Overzee, H. M. J. Factsheet: Sampling demersal active fisheries through self-sampling (DEMACT2). Stichting Wageningen Research, Centre for Fisheries Research (CVO), 2023. http://dx.doi.org/10.18174/634040.

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Geist, William H. IAEA Sampling Plan. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1392849.

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Mclean, Thomas Donaldson. Air Sampling Equipment. Office of Scientific and Technical Information (OSTI), July 2018. http://dx.doi.org/10.2172/1458969.

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Colton, David, and Peter Monk. Linear Sampling Method. Fort Belvoir, VA: Defense Technical Information Center, March 1999. http://dx.doi.org/10.21236/ada368321.

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Hodur, Richard M. Monterey Bay Sampling. Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada628599.

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