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Tesi sul tema "Density estimation"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 25 luglio 2025

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1

Wang, Xiaoxia. "Manifold aligned density estimation." Thesis, University of Birmingham, 2010. http://etheses.bham.ac.uk//id/eprint/847/.

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With the advent of the information technology, the amount of data we are facing today is growing in both the scale and the dimensionality dramatically. It thus raises new challenges for some traditional machine learning tasks. This thesis is mainly concerned with manifold aligned density estimation problems. In particular, the work presented in this thesis includes efficiently learning the density distribution on very large-scale datasets and estimating the manifold aligned density through explicit manifold modeling. First, we propose an efficient and sparse density estimator: Fast Parzen Wind

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2

Rademeyer, Estian. "Bayesian kernel density estimation." Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/64692.

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This dissertation investigates the performance of two-class classi cation credit scoring data sets with low default ratios. The standard two-class parametric Gaussian and naive Bayes (NB), as well as the non-parametric Parzen classi ers are extended, using Bayes' rule, to include either a class imbalance or a Bernoulli prior. This is done with the aim of addressing the low default probability problem. Furthermore, the performance of Parzen classi cation with Silverman and Minimum Leave-one-out Entropy (MLE) Gaussian kernel bandwidth estimation is also investigated. It is shown that the n

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3

Stride, Christopher B. "Semi-parametric density estimation." Thesis, University of Warwick, 1995. http://wrap.warwick.ac.uk/109619/.

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The local likelihood method of Copas (1995a) allows for the incorporation into our parametric model of influence from data local to the point t at which we are estimating the true density function g(t). This is achieved through an analogy with censored data; we define the probability of a data point being considered observed, given that it has taken value xi, as where K is a scaled kernel function with smoothing parameter h. This leads to a likelihood function which gives more weight to observations close to t, hence the term ‘local likelihood’. After constructing this local likelihood functio

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4

Rossiter, Jane E. "Epidemiological applications of density estimation." Thesis, University of Oxford, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.291543.

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5

Sung, Iyue. "Importance sampling kernel density estimation /." The Ohio State University, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=osu1486398528559777.

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6

Kile, Håkon. "Bandwidth Selection in Kernel Density Estimation." Thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-10015.

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<p>In kernel density estimation, the most crucial step is to select a proper bandwidth (smoothing parameter). There are two conceptually different approaches to this problem: a subjective and an objective approach. In this report, we only consider the objective approach, which is based upon minimizing an error, defined by an error criterion. The most common objective bandwidth selection method is to minimize some squared error expression, but this method is not without its critics. This approach is said to not perform satisfactory in the tail(s) of the density, and to put too much weight on o

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7

Achilleos, Achilleas. "Deconvolution kernal density and regression estimation." Thesis, University of Bristol, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.544421.

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8

Buchman, Susan. "High-Dimensional Adaptive Basis Density Estimation." Research Showcase @ CMU, 2011. http://repository.cmu.edu/dissertations/169.

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In the realm of high-dimensional statistics, regression and classification have received much attention, while density estimation has lagged behind. Yet there are compelling scientific questions which can only be addressed via density estimation using high-dimensional data, such as the paths of North Atlantic tropical cyclones. If we cast each track as a single high-dimensional data point, density estimation allows us to answer such questions via integration or Monte Carlo methods. In this dissertation, I present three new methods for estimating densities and intensities for high-dimensional d

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9

Lu, Shan. "Essays on volatility forecasting and density estimation." Thesis, University of Aberdeen, 2019. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=240161.

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This thesis studies two subareas within the forecasting literature: volatility forecasting and risk-neutral density estimation and asks the question of how accurate volatility forecasts and risk-neutral density estimates can be made based on the given information. Two sources of information are employed to make those forecasts: historical information contained in time series of asset prices, and forward-looking information embedded in prices of traded options. Chapter 2 tests the comparative performance of two volatility scaling laws - the square-root-of-time (√T) and an empirical law, TH, cha

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10

Chan, Kwokleung. "Bayesian learning in classification and density estimation /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC IP addresses, 2002. http://wwwlib.umi.com/cr/ucsd/fullcit?p3061619.

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11

Suaray, Kagba N. "On kernel density estimation for censored data /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2004. http://wwwlib.umi.com/cr/ucsd/fullcit?p3144346.

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12

Mao, Ruixue. "Road Traffic Density Estimation in Vehicular Network." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9467.

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In recent decades, vehicular networks or intelligent transportation systems are being increasingly investigated and used to provide solutions to next generation traffic systems. Road traffic density estimation provides important information for road planning, intelligent road routing, road traffic control, vehicular network traffic scheduling, routing and dissemination. The ever increasing number of vehicles equipped with wireless communication capabilities provide new means to estimate the road traffic density more accurately and in real time than traditionally used techniques. In this thesis

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13

Chee, Chew–Seng. "A mixture-based framework for nonparametric density estimation." Thesis, University of Auckland, 2011. http://hdl.handle.net/2292/10148.

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The primary goal of this thesis is to provide a mixture-based framework for nonparametric density estimation. This framework advocates the use of a mixture model with a nonparametric mixing distribution to approximate the distribution of the data. The implementation of a mixture-based nonparametric density estimator generally requires the specification of parameters in a mixture model and the choice of the bandwidth parameter. Consequently, a nonparametric methodology consisting of both the estimation and selection steps is described. For the estimation of parameters in mixture models, we empl

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14

Kharoufeh, Jeffrey P. "Density estimation for functions of correlated random variables." Ohio : Ohio University, 1997. http://www.ohiolink.edu/etd/view.cgi?ohiou1177097417.

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15

Nasios, Nikolaos. "Bayesian learning for parametric and kernel density estimation." Thesis, University of York, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.428460.

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16

Finch, Andrew M. "Density estimation for pattern recognition using neural networks." Thesis, University of York, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.261061.

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17

Lee, Suhwon. "Nonparametric bayesian density estimation with intrinsic autoregressive priors /." free to MU campus, to others for purchase, 2003. http://wwwlib.umi.com/cr/mo/fullcit?p3115565.

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18

Li, Yuhao. "Multiclass Density Estimation Analysis in N-Dimensional Space featuring Delaunay Tessellation Field Estimation." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-301958.

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Multiclass density estimation is a method that can both estimate the density of a field and classify a given point to its targeted class. Delaunay Tessellation Field Estimation is a tessellation based multiclass density estimation technique that has recently been resurfaced and has been applied in the field of astronomy and computer science. In this paper Delaunay Tessellation Field Estimation is compared with other traditional density estimation techniques such as Kernel Density Estimation, k-Nearest Neighbour Density, Local Reachability Density and histogram to deliver a detailed performance

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19

Leahy, Logan Patrick. "Estimating output torque via amplitude estimation and neural drive : a high-density sEMG study." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127134.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, May, 2020<br>Cataloged from the official PDF of thesis.<br>Includes bibliographical references (pages 115-121).<br>The scope and relevance of wearable robotics spans across a number of research fields with a variety of applications. One such application is the augmentation of healthy individuals for improved performance. A challenge within this field is improving user-interface control. An established approach for improving user-interface control is neural control interfaces derived from surface electrom

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20

Minsker, Stanislav. "Non-asymptotic bounds for prediction problems and density estimation." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44808.

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This dissertation investigates the learning scenarios where a high-dimensional parameter has to be estimated from a given sample of fixed size, often smaller than the dimension of the problem. The first part answers some open questions for the binary classification problem in the framework of active learning. Given a random couple (X,Y) with unknown distribution P, the goal of binary classification is to predict a label Y based on the observation X. Prediction rule is constructed from a sequence of observations sampled from P. The concept of active learning can be informally characterized as f

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21

Cule, Madeleine. "Maximum likelihood estimation of a multivariate log-concave density." Thesis, University of Cambridge, 2010. https://www.repository.cam.ac.uk/handle/1810/237061.

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Density estimation is a fundamental statistical problem. Many methods are eithersensitive to model misspecification (parametric models) or difficult to calibrate, especiallyfor multivariate data (nonparametric smoothing methods). We propose an alternativeapproach using maximum likelihood under a qualitative assumption on the shape ofthe density, specifically log-concavity. The class of log-concave densities includes manycommon parametric families and has desirable properties. For univariate data, theseestimators are relatively well understood, and are gaining in popularity in theory andpractic

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22

Mulye, Apoorva. "Power Spectrum Density Estimation Methods for Michelson Interferometer Wavemeters." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35500.

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In Michelson interferometry, many algorithms are used to detect the number of active laser sources at any given time. Conventional FFT-based non-parametric methods are widely used for this purpose. However, non-parametric methods are not the only possible option to distinguish the peaks in a spectrum, as these methods are not the most suitable methods for short data records and for closely spaced wavelengths. This thesis aims to provide solutions to these problems. It puts forward the use of parametric methods such as autoregressive methods and harmonic methods, and proposes two new algorithms

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23

Sardo, Lucia. "Model selection in probability density estimation using Gaussian mixtures." Thesis, University of Surrey, 1997. http://epubs.surrey.ac.uk/842833/.

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This thesis proposes Gaussian Mixtures as a flexible semiparametric tool for density estimation and addresses the problem of model selection for this class of density estimators. First, a brief introduction to various techniques for model selection proposed in literature is given. The most commonly used techniques are cross validation nad methods based on data reuse and they all are either computationally very intensive or extremely demanding in terms of training set size. Another class of methods known as information criteria allows model selection at a much lower computational cost and for a

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24

Amghar, Mohamed. "Multiscale local polynomial transforms in smoothing and density estimation." Doctoral thesis, Universite Libre de Bruxelles, 2017. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/262040.

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Un défi majeur dans les méthodes d'estimation non linéaire multi-échelle, comme le seuillage des ondelettes, c'est l'extension de ces méthodes vers une disposition où les observations sont irrégulières et non équidistantes. L'application de ces techniques dans le lissage de données ou l'estimation des fonctions de densité, il est crucial de travailler dans un espace des fonctions qui impose un certain degré de régularité. Nous suivons donc une approche différente, en utilisant le soi-disant système de levage. Afin de combiner la régularité et le bon conditionnement numérique, nous adoptons un

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25

Inacio, Marco Henrique de Almeida. "Comparing two populations using Bayesian Fourier series density estimation." Universidade Federal de São Carlos, 2017. https://repositorio.ufscar.br/handle/ufscar/8920.

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Submitted by Aelson Maciera (aelsoncm@terra.com.br) on 2017-06-28T18:26:17Z No. of bitstreams: 1 DissMHAI.pdf: 1513128 bytes, checksum: 1bb98ae57371ab00d2c86311b02054cb (MD5)<br>Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-08-07T17:53:27Z (GMT) No. of bitstreams: 1 DissMHAI.pdf: 1513128 bytes, checksum: 1bb98ae57371ab00d2c86311b02054cb (MD5)<br>Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-08-07T17:53:36Z (GMT) No. of bitstreams: 1 DissMHAI.pdf: 1513128 bytes, checksum: 1bb98ae57371ab00d2c86311b02054cb (MD5)<br>Made available in

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26

Wright, George Alfred Jr. "Nonparameter density estimation and its application in communication theory." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/14979.

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27

Chan, Karen Pui-Shan. "Kernel density estimation, Bayesian inference and random effects model." Thesis, University of Edinburgh, 1990. http://hdl.handle.net/1842/13350.

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This thesis contains results of a study in kernel density estimation, Bayesian inference and random effects models, with application to forensic problems. Estimation of the Bayes' factor in a forensic science problem involved the derivation of predictive distributions in non-standard situations. The distribution of the values of a characteristic of interest among different items in forensic science problems is often non-Normal. Background, or training, data were available to assist in the estimation of the distribution for measurements on cat and dog hairs. An informative prior, based on the k

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28

Joshi, Niranjan Bhaskar. "Non-parametric probability density function estimation for medical images." Thesis, University of Oxford, 2008. http://ora.ox.ac.uk/objects/uuid:ebc6af07-770b-4fee-9dc9-5ebbe452a0c1.

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The estimation of probability density functions (PDF) of intensity values plays an important role in medical image analysis. Non-parametric PDF estimation methods have the advantage of generality in their application. The two most popular estimators in image analysis methods to perform the non-parametric PDF estimation task are the histogram and the kernel density estimator. But these popular estimators crucially need to be ‘tuned’ by setting a number of parameters and may be either computationally inefficient or need a large amount of training data. In this thesis, we critically analyse and f

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29

Inácio, Marco Henrique de Almeida. "Comparing two populations using Bayesian Fourier series density estimation." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/104/104131/tde-12092017-083813/.

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Given two samples from two populations, one could ask how similar the populations are, that is, how close their probability distributions are. For absolutely continuous distributions, one way to measure the proximity of such populations is to use a measure of distance (metric) between the probability density functions (which are unknown given that only samples are observed). In this work, we work with the integrated squared distance as metric. To measure the uncertainty of the squared integrated distance, we first model the uncertainty of each of the probability density functions using a nonpa

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30

Ellis, Amanda Morgan. "An assessment of density estimation methods for forest ungulates." Thesis, Rhodes University, 2004. http://hdl.handle.net/10962/d1007830.

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The development of conservation and management programs for an animal population relies on a knowledge of the number of individuals in an area. In order to achieve reliable estimates, precise and accurate techniques for estimating population densities are needed. This study compared the use of direct and indirect methods of estimating kudu (Trage/aphus strepsiceras), bush buck (Trage/aphus scriptus), common duiker (Sy/vicapra grimmia), and blue duiker (Philantamba manticala) densities on Shamwari Game Reserve in the Eastern Cape Province, South Africa. These species prefer habitats of dense fo

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31

Thomas, Derek C. "Theory and Estimation of Acoustic Intensity and Energy Density." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2560.pdf.

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32

Jawhar, Nizar Sami. "Adaptive Density Estimation Based on the Mode Existence Test." DigitalCommons@USU, 1996. https://digitalcommons.usu.edu/etd/7129.

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The kernel persists as the most useful tool for density estimation. Although, in general, fixed kernel estimates have proven superior to results of available variable kernel estimators, Minnotte's mode tree and mode existence test give us newfound hope of producing a useful adaptive kernel estimator that triumphs when the fixed kernel methods fail. It improves on the fixed kernel in multimodal distributions where the size of modes is unequal, and where the degree of separation of modes varies. When these latter conditions exist, they present a serious challenge to the best of fixed kernel dens

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33

Baba, Harra M'hammed. "Estimation de densités spectrales d'ordre élevé." Rouen, 1996. http://www.theses.fr/1996ROUES023.

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Dans cette thèse nous construisons des estimateurs de la densité spectrale du cumulant, pour un processus strictement homogène et centré, l'espace des temps étant l'espace multidimensionnel, euclidien réel ou l'espace multidimensionnel des nombres p-adiques. Dans cette construction nous avons utilisé la méthode de lissage de la trajectoire et un déplacement dans le temps ou la méthode de fenêtres spectrales. Sous certaines conditions de régularité, les estimateurs proposés sont asymptotiquement sans biais et convergents. Les procédures d'estimation exposées peuvent trouver des applications dan

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34

Uria, Benigno. "Connectionist multivariate density-estimation and its application to speech synthesis." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/15868.

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Autoregressive models factorize a multivariate joint probability distribution into a product of one-dimensional conditional distributions. The variables are assigned an ordering, and the conditional distribution of each variable modelled using all variables preceding it in that ordering as predictors. Calculating normalized probabilities and sampling has polynomial computational complexity under autoregressive models. Moreover, binary autoregressive models based on neural networks obtain statistical performances similar to that of some intractable models, like restricted Boltzmann machines, on

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35

Pawluczyk, Olga. "Volumetric estimation of breast density for breast cancer risk prediction." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ58694.pdf.

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36

Kelly, Robert 1969. "Estimation of iceberg density in the Grand Banks of Newfoundland." Thesis, McGill University, 1996. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=23746.

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Icebergs offshore Newfoundland represent hazards to both ships and constructed facilities, such as off-shore oil production facilities. Collision with icebergs represent hazards for both surface and sub-surface facilities. In the latter case, hazards are associated with seabed scouring by the iceberg keel. In both cases, hazard analysis requires estimation of the flux of icebergs and their size distribution. Estimates of the flux of icebergs can be achieved by obtaining separate estimates of iceberg densities and of drift patterns of iceberg velocities. The objective of this thesis is to devel

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37

Hazelton, Martin Luke. "Method of density estimation with application to Monte Carlo methods." Thesis, University of Oxford, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.334850.

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38

Bugrien, Jamal B. "Robust approaches to clustering based on density estimation and projection." Thesis, University of Leeds, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.418939.

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39

Braga, Ígor Assis. "Stochastic density ratio estimation and its application to feature selection." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-07042015-142545/.

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The estimation of the ratio of two probability densities is an important statistical tool in supervised machine learning. In this work, we introduce new methods of density ratio estimation based on the solution of a multidimensional integral equation involving cumulative distribution functions. The resulting methods use the novel V -matrix, a concept that does not appear in previous density ratio estimation methods. Experiments demonstrate the good potential of this new approach against previous methods. Mutual Information - MI - estimation is a key component in feature selection and essential

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40

Alquier, Pierre. "Transductive and inductive adaptative inference for regression and density estimation." Paris 6, 2006. http://www.theses.fr/2006PA066436.

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Inférence Adaptative, Inductive et Transductive, pour l'Estimation de la Régression et de la Densité (Pierre Alquier) Cette thèse a pour objet l'étude des propriétés statistiques de certains algorithmes d'apprentissage dans le cas de l'estimation de la régression et de la densité. Elle est divisée en trois parties. La première partie consiste en une généralisation des théorèmes PAC-Bayésiens, sur la classification, d'Olivier Catoni, au cas de la régression avec une fonction de perte générale. Dans la seconde partie, on étudie plus particulièrement le cas de la régression aux moindres carrés et

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41

McDonagh, Steven George. "Building models from multiple point sets with kernel density estimation." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/10568.

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One of the fundamental problems in computer vision is point set registration. Point set registration finds use in many important applications and in particular can be considered one of the crucial stages involved in the reconstruction of models of physical objects and environments from depth sensor data. The problem of globally aligning multiple point sets, representing spatial shape measurements from varying sensor viewpoints, into a common frame of reference is a complex task that is imperative due to the large number of critical functions that accurate and reliable model reconstructions con

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42

Zhu, Hui. "Scatterer number density estimation for tissue characterization in ultrasound imaging /." Online version of thesis, 1990. http://hdl.handle.net/1850/10882.

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43

Wong, Kam-wah. "Efficient computation of global illumination based on adaptive density estimation /." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B25151083.

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44

Wang, Yi. "Latent tree models for multivariate density estimation : algorithms and applications /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20WANGY.

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45

Esterhuizen, Gerhard. "Generalised density function estimation using moments and the characteristic function." Thesis, Link to the online version, 2003. http://hdl.handle.net/10019.1/1001.

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46

Sain, Stephan R. "Adaptive kernel density estimation." Thesis, 1994. http://hdl.handle.net/1911/16743.

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The need for improvements over the fixed kernel density estimator in certain situations has been discussed extensively in the literature, particularly in the application of density estimation to mode hunting. Problem densities often exhibit skewness or multimodality with differences in scale for each mode. By varying the bandwidth in some fashion, it is possible to achieve significant improvements over the fixed bandwidth approach. In general, variable bandwidth kernel density estimators can be divided into two categories: those that vary the bandwidth with the estimation point (balloon estima

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47

Gebert, Mark Allen. "Nonparametric density contour estimation." Thesis, 1998. http://hdl.handle.net/1911/19261.

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Estimation of the level sets for an unknown probability density is done with no specific assumed form for that density, that is, non-parametrically. Methods for tackling this problem are presented. Earlier research showed existence and properties of an estimate based on a kernel density estimate in one dimension. Monte Carlo methods further demonstrated the reasonability of extending this approach to two dimensions. An alternative procedure is now considered that focuses on properties of the contour itself; procedures wherein we define and make use of an objective function based on the charact

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48

莊宗霖. "An Approach on Function Estimation and Density Estimation." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/92765889113139635622.

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碩士<br>國立中正大學<br>數理統計研究所<br>91<br>A recent approach using argument on expectation of random variables for estimation of unknown functional values based on some known values of the function at various points is investigated by way of empirical simulation. The approach can be applied to do estimation on probability density functions based on random samples from the assumed distribution. Theoretical formulations are presented to express the estimators in each case. Such estimators are more extensive than the traditional kernel type estimators for estimating unknown functions and probability densit

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49

Yao, Bo-Yuan, and 姚博元. "Density Estimation by Spline Smoothing." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/03542027112847534183.

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50

Lin, Mu. "Nonparametric density estimation via regularization." 2009. http://hdl.handle.net/10048/709.

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Abstract (sommario):

Thesis (M. Sc.)--University of Alberta, 2009.<br>Title from pdf file main screen (viewed on Dec. 11, 2009). "A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science in Statistics, Department of Mathematical and Statistical Sciences, University of Alberta." Includes bibliographical references.

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