Relevant bibliographies by topics / Bayesian interpretation

Academic literature on the topic 'Bayesian interpretation'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Bayesian interpretation"

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Listokin, Yair. "Bayesian Contractual Interpretation." Journal of Legal Studies 39, no. 2 (June 2010): 359–74. http://dx.doi.org/10.1086/652459.

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Giovannelli, J. F., and J. Idier. "Bayesian interpretation of periodograms." IEEE Transactions on Signal Processing 49, no. 7 (July 2001): 1388–96. http://dx.doi.org/10.1109/78.928692.

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Kumar, V. P., and U. B. Desai. "Image interpretation using Bayesian networks." IEEE Transactions on Pattern Analysis and Machine Intelligence 18, no. 1 (1996): 74–77. http://dx.doi.org/10.1109/34.476423.

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Jones, W. Paul. "Bayesian Interpretation of Test Reliability." Educational and Psychological Measurement 51, no. 3 (September 1991): 627–35. http://dx.doi.org/10.1177/0013164491513009.

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THORNDIKE, ROBERT L. "Bayesian Concepts and Test Interpretation." Journal of Counseling & Development 65, no. 3 (November 1986): 170–71. http://dx.doi.org/10.1002/j.1556-6676.1986.tb01269.x.

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Cho, Hyun Ja, Eun Young Kwack, and Chul Soon Choi. "Bayesian approach in interpretation of mammography." Journal of the Korean Radiological Society 27, no. 6 (1991): 901. http://dx.doi.org/10.3348/jkrs.1991.27.6.901.

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Kim, Sang Joon. "Bayesian interpretation of eyewitness statement data." KOREAN JOURNAL OF FORENSIC PSYCHOLOGY 8, no. 2 (July 31, 2017): 61–110. http://dx.doi.org/10.53302/kjfp.2017.07.8.2.61.

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Elble, Rodger J. "Bayesian Interpretation of Essential Tremor Plus." Journal of Clinical Neurology 18, no. 2 (2022): 127. http://dx.doi.org/10.3988/jcn.2022.18.2.127.

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Wan, E. A. "Neural network classification: a Bayesian interpretation." IEEE Transactions on Neural Networks 1, no. 4 (1990): 303–5. http://dx.doi.org/10.1109/72.80269.

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Laganière, Robert, and Amar Mitiche. "Direct Bayesian interpretation of visual motion." Robotics and Autonomous Systems 14, no. 4 (June 1995): 247–54. http://dx.doi.org/10.1016/0921-8890(94)00018-w.

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Dissertations / Theses on the topic "Bayesian interpretation"

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Christen, José Andrés. "Bayesian interpretation of radiocarbon results." Thesis, University of Nottingham, 1994. http://eprints.nottingham.ac.uk/11035/.

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Over the last thirty years radiocarbon dating has been widely used in archaeology and related fields to address a wide-range of chronological questions. Because of some inherent stochastic factors of a complex nature, radiocarbon dating presents a rich source of challenging statistical problems. The chronological questions posed commonly involve the interpretation of groups of radiocarbon determinations and often substantial amounts of a priori information are available. The statistical techniques used up to very recently could only deal with the analysis of one determination at a time, and no prior information could be included in the analysis. However, over the last few years some problems have been successfully tackled using the Bayesian paradigm. In this thesis we expand that work and develop a general statistical framework for the Bayesian interpretation of radiocarbon determinations. Firstly we consider the problem of radiocarbon calibration and develop a novel approach. Secondly we develop a statistical framework which permits the inclusion of prior archaeological knowledge and illustrate its use with a wide range of examples. We discuss various generic problems some of which are, replications, summarisation, floating chronologies and archaeological phase structures. The techniques used to obtain the posterior distributions of interest are numerical and, in most of the cases, we have used Markov chain Monte Carlo (MCMC) methods. We also discuss the sampling routines needed for the implementation of the MCNIC methods used in our examples. Thirdly we address the very important problem of outliers in radiocarbon dating and develop an original methodology for the identification of outliers in sets of radiocarbon determinations. We show how our framework can be extended to permit the identification of outliers. Finally we apply this extended framework to the analysis of a substantial archaeological dating problem.
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Calder, Brian. "Bayesian spatial models for SONAR image interpretation." Thesis, Heriot-Watt University, 1997. http://hdl.handle.net/10399/1249.

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This thesis is concerned with the utilisation of spatial information in processing of high-frequency sidescan SONAR imagery, and particularly in how such information can be used in developing techniques to assist in mapping functions. Survey applications aim to generate maps of the seabed, but are time consuming and expensive; automatic processing is required to improve efficiency. Current techniques have had some success, but utilise little of the available spatial information. Previously, inclusion of such knowledge was prohibitively expensive; recent improvements in numerical simulations techniques has reduced the costs involved. This thesis attempts to exploit these improvements into a method for including spatial information in SONAR processing and in general to image and signal analysis. Bayesian techniques for inclusion of prior knowledge and structuring complex problems are developed and applied to problems of texture segmentation, object detection and parameter extraction. It is shown through experiments on groundtruth and real datasets that the inclusion of spatial context can be very effective in improving poor techniques or, conversely in allowing simpler techniques to be used with the same objective outcome (with obvious computational advantages). The thesis also considers some of the implementation problems with the techniques used, and develops simple modifications to improve common algorithms.
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Maimon, Geva. "A Bayesian approach to the statistical interpretation of DNA evidence." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=92221.

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This dissertation sets forth a foundation for a continuous model for the interpretation of DNA mixture evidence. We take a new approach to modelling electropherogram data by modelling the actual electropherogram as a curve rather than modelling the allelic peak areas under the curve. This shift allows us to retain all the data available and to bypass the approximation of peak areas by GeneMapper R (Applied Biosystems, 2003). The two problems associated with the use of this programme - prohibitive costs and patented processes - are thus avoided.
To establish a model for electropherogram data, we explore two Bayesian wavelet approaches to modelling functions (Chipman et al., 1997 ; M. Clyde et al., 1998) as well as a Bayesian Adaptive Regression Splines approach (DiMatteo et al., 2001). Furthermore, we establish our own genotyping algorithm, once again circumventing the need for GeneMapper R, and obtain posterior probabilities for the resulting genotypes.
With a model in place for single-source DNA samples, we develop an algorithm that deconvolves a two-person mixture into its separate components and provides the posterior probabilities for the resulting genotype combinations.
In addition, because of the widely recognized need to perform further research on continuous models in mixture interpretation and the difficulty in obtaining the necessary data to do so (due to privacy laws and laboratory restrictions), a tool for simulating realistic data is of the utmost importance. PCRSIM (Gill et al., 2005) is the most popular simulation software for this purpose. We propose a method for refining the parameter estimates used in PCRSIM in order to simulate more accurate data.
Cette dissertation établit les fondations nécessaires à la création d'un modèle continu servant à l'interprétation des échantillons d'ADN à sources multiples (mélanges). Nous prenons une nouvelle approche de la modélisation des données d'´electrophérogrammes en modélisant l'électrophérogramme en tant que courbe plutôt que de modéliser l'aire sous la courbe des sommets alléliques. Cette approche nous permet de conserver toutes les données disponibles et d'éviter l'estimation de l'aire sous la courbe au moyen de GeneMapper R (Applied Biosystems, 2003). Deux problèmes associés à l'utilisation de ce programme - des coûts prohibitifs et une procédure brevetée - sont ainsi évités.
Afin d'établir un modèle pour les données d'électrophérogramme, nous explorons deux approches bayésiennes pour la modélisation des fonctions par ondelettes (Chipman et al., 1997 ; M. Clyde et al., 1998) de même qu'une approche connue sous le nom de Bayesian Adaptive Regression Splines (DiMatteo et al., 2001). De plus, nous élaborons notre propre algorithme pour l'analyse des génotypes, nous permettant, encore une fois, d'éviter GeneMapper R, et d'obtenir les probabilités postérieures des génotypes résultants.
À l'aide d'un modèle d'échantillon d'ADN à source unique, nous développons un algorithme qui divise un échantillon de deux personnes en ses composantes séparées et estime les probabilités postérieures des différentes combinaisons possibles de génotype.
De plus, en raison des lacunes dans la littérature sur les modèles continus pour l'analyse d'échantillons d'ADN à sources multiples et de la difficulté à obtenir les données n´ecessaire pour l'effectuer (en raison des lois sur la protection de la vie privée et des restrictions en laboratoire), un outil qui simule des données réalistes est de la plus grande importance. PCRSIM (Gill et al., 2005) est un outil qui permet de répondre à ce besoin. Par cet outil, nous proposons une méthode pour raffiner les estimations des paramètres afin de simuler des données plus précises.
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Haan, Benjamin J. "Decomposing Bayesian network representations of distributed sensor interpretation problems using weighted average conditional mutual information /." Available to subscribers only, 2007. http://proquest.umi.com/pqdweb?did=1421626381&sid=1&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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Bringmann, Oliver. "Symbolische Interpretation Technischer Zeichnungen." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2003. http://nbn-resolving.de/urn:nbn:de:swb:14-1045648731734-96098.

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Gescannte und vektorisierte technische Zeichnungen werden automatisch unter Nutzung eines Netzes von Modellen in eine hochwertige Datenstruktur migriert. Die Modelle beschreiben die Inhalte der Zeichnungen hierarchisch und deklarativ. Modelle für einzelne Bestandteile der Zeichnungen können paarweise unabhängig entwickelt werden. Dadurch werden auch sehr komplexe Zeichnungsklassen wie Elektroleitungsnetze oder Gebäudepläne zugänglich. Die Modelle verwendet der neue, sogenannte Y-Algorithmus: Hypothesen über die Deutung lokaler Zeichnungsinhalte werden hierarchisch generiert. Treten bei der Nutzung konkurrierender Modelle Konflikte auf, werden diese protokolliert. Mittels des Konfliktbegriffes können konsistente Interpretationen einer kompletten Zeichnung abstrakt definiert und während der Analyse einer konkreten Zeichnung bestimmt werden. Ein wahrscheinlichkeitsbasiertes Gütemaß bewertet jede dieser alternativen, globalen Interpretationen. Das Suchen einer bzgl. dieses Maßes optimalen Interpretation ist ein NP-hartes Problem. Ein Branch and Bound-Algorithmus stellt die adäquate Lösung dar.
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Bringmann, Oliver. "Symbolische Interpretation Technischer Zeichnungen." Doctoral thesis, Technische Universität Dresden, 2001. https://tud.qucosa.de/id/qucosa%3A24202.

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Gescannte und vektorisierte technische Zeichnungen werden automatisch unter Nutzung eines Netzes von Modellen in eine hochwertige Datenstruktur migriert. Die Modelle beschreiben die Inhalte der Zeichnungen hierarchisch und deklarativ. Modelle für einzelne Bestandteile der Zeichnungen können paarweise unabhängig entwickelt werden. Dadurch werden auch sehr komplexe Zeichnungsklassen wie Elektroleitungsnetze oder Gebäudepläne zugänglich. Die Modelle verwendet der neue, sogenannte Y-Algorithmus: Hypothesen über die Deutung lokaler Zeichnungsinhalte werden hierarchisch generiert. Treten bei der Nutzung konkurrierender Modelle Konflikte auf, werden diese protokolliert. Mittels des Konfliktbegriffes können konsistente Interpretationen einer kompletten Zeichnung abstrakt definiert und während der Analyse einer konkreten Zeichnung bestimmt werden. Ein wahrscheinlichkeitsbasiertes Gütemaß bewertet jede dieser alternativen, globalen Interpretationen. Das Suchen einer bzgl. dieses Maßes optimalen Interpretation ist ein NP-hartes Problem. Ein Branch and Bound-Algorithmus stellt die adäquate Lösung dar.
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LeSage, James P., and Manfred M. Fischer. "Spatial Growth Regressions: Model Specification, Estimation and Interpretation." WU Vienna University of Economics and Business, 2007. http://epub.wu.ac.at/3968/1/SSRN%2Did980965.pdf.

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This paper uses Bayesian model comparison methods to simultaneously specify both the spatial weight structure and explanatory variables for a spatial growth regression involving 255 NUTS 2 regions across 25 European countries. In addition, a correct interpretation of the spatial regression parameter estimates that takes into account the simultaneous feed- back nature of the spatial autoregressive model is provided. Our findings indicate that incorporating model uncertainty in conjunction with appropriate parameter interpretation decreased the importance of explanatory variables traditionally thought to exert an important influence on regional income growth rates. (authors' abstract)
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Klukowski, Piotr. "Nuclear magnetic resonance spectroscopy interpretation for protein modeling using computer vision and probabilistic graphical models." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4720.

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Dynamic development of nuclear magnetic resonance spectroscopy (NMR) allowed fast acquisition of experimental data which determine structure and dynamics of macromolecules. Nevertheless, due to lack of appropriate computational methods, NMR spectra are still analyzed manually by researchers what takes weeks or years depending on protein complexity. Therefore automation of this process is extremely desired and can significantly reduce time of protein structure solving. In presented work, a new approach to automated three-dimensional protein NMR spectra analysis is presented. It is based on Histogram of Oriented Gradients and Bayesian Network which have not been ever applied in that context in the history of research in the area. Proposed method was evaluated using benchmark data which was established by manual labeling of 99 spectroscopic images taken from 6 different NMR experiments. Afterwards subsequent validation was made using spectra of upstream of N-ras protein. With the use of proposed method, a three-dimensional structure of mentioned protein was calculated. Comparison with reference structure from protein databank reveals no significant differences what has proven that proposed method can be used in practice in NMR laboratories.
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Button, Zach. "The application and interpretation of the two-parameter item response model in the context of replicated preference testing." Kansas State University, 2015. http://hdl.handle.net/2097/20113.

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Master of Science
Statistics
Suzanne Dubnicka
Preference testing is a popular method of determining consumer preferences for a variety of products in areas such as sensory analysis, animal welfare, and pharmacology. However, many prominent models for this type of data do not allow different probabilities of preferring one product over the other for each individual consumer, called overdispersion, which intuitively exists in real-world situations. We investigate the Two-Parameter variation of the Item Response Model (IRM) in the context of replicated preference testing. Because the IRM is most commonly applied to multiple-choice testing, our primary focus is the interpretation of the model parameters with respect to preference testing and the evaluation of the model’s usefulness in this context. We fit a Bayesian version of the Two-Parameter Probit IRM (2PP) to two real-world datasets, Raisin Bran and Cola, as well as five hypothetical datasets constructed with specific parameter properties in mind. The values of the parameters are sampled via the Gibbs Sampler and examined using various plots of the posterior distributions. Next, several different models and prior distribution specifications are compared over the Raisin Bran and Cola datasets using the Deviance Information Criterion (DIC). The Two-Parameter IRM is a useful tool in the context of replicated preference testing, due to its ability to accommodate overdispersion, its intuitive interpretation, and its flexibility in terms of parameterization, link function, and prior specification. However, we find that this model brings computational difficulties in certain situations, some of which require creative solutions. Although the IRM can be interpreted for replicated preference testing scenarios, this data typically contains few replications, while the model was designed for exams with many items. We conclude that the IRM may provide little evidence for marketing decisions, and it is better-suited for exploring the nature of consumer preferences early in product development.
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Li, Bin. "Statistical learning and predictive modeling in data mining." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1155058111.

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Books on the topic "Bayesian interpretation"

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B, Desai Uday, ed. Bayesian approach to image interpretation. Boston: Kluwer Academic Publishers, 2001.

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Natalie, Hicks Tacha, and Buckleton John S, eds. Forensic interpretation of glass evidence. Boca Raton: CRC Press, 2000.

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Bayesian methods in diagnostic medicine. Boca Raton: Taylor & Francis, 2007.

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Kopparapu, Sunil K., and Uday B. Desai. Bayesian Approach to Image Interpretation. Springer, 2013.

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Kopparapu, Sunil K., and Uday B. Desai. Bayesian Approach to Image Interpretation. Springer London, Limited, 2006.

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Bayesian Approach to Image Interpretation. Boston: Kluwer Academic Publishers, 2002. http://dx.doi.org/10.1007/b117231.

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Curran, James Michael, John S. Buckleton, and Tacha Natalie Hicks Champod. Forensic Interpretation of Glass Evidence. Taylor & Francis Group, 2000.

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(Editor), James Michael Curran, Tacha Natalie Hicks Champod (Editor), and John S. Buckleton (Editor), eds. Forensic Interpretation of Glass Evidence. CRC, 2000.

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Curran, James Michael, John S. Buckleton, and Tacha Natalie Hicks Champod. Forensic Interpretation of Glass Evidence. Taylor & Francis Group, 2000.

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Curran, James Michael. Forensic Interpretation of Glass Evidence. Taylor & Francis Group, 2010.

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Book chapters on the topic "Bayesian interpretation"

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van Oijen, Marcel. "After the Calibration: Interpretation, Reporting, Visualization." In Bayesian Compendium, 77–80. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55897-0_11.

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Pillonetto, Gianluigi, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, and Lennart Ljung. "Bayesian Interpretation of Regularization." In Regularized System Identification, 95–134. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95860-2_4.

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AbstractIn the previous chapter, it has been shown that the regularization approach is particularly useful when information contained in the data is not sufficient to obtain a precise estimate of the unknown parameter vector and standard methods, such as least squares, yield poor solutions. The fact itself that an estimate is regarded as poor suggests the existence of some form of prior knowledge on the degree of acceptability of candidate solutions. It is this knowledge that guides the choice of the regularization penalty that is added as a corrective term to the usual sum of squared residuals. In the previous chapters, this design process has been described in a deterministic setting where only the measurement noises are random. In this chapter, we will see that an alternative formalization of prior information is obtained if a subjective/Bayesian estimation paradigm is adopted. The major difference is that the parameters, rather than being regarded as deterministic, are now treated as a random vector. This stochastic setting permits the definition of new powerful tools for both priors selection, e.g., through the maximum entropy principle, and for regularization parameters tuning, e.g., through the empirical Bayes approach and its connection with the concept of equivalent degrees of freedom.
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Zeevat, Henk. "Bayesian NL Interpretation and Learning." In Logic, Language, and Computation, 342–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22303-7_22.

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Zeevat, Henk. "Bayesian interpretation and Optimality Theory." In Linguistik Aktuell/Linguistics Today, 191–220. Amsterdam: John Benjamins Publishing Company, 2011. http://dx.doi.org/10.1075/la.180.08zee.

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Buck, Caitlin E. "Bayesian Chronological Data Interpretation: Where Now?" In Lecture Notes in Statistics, 1–24. London: Springer London, 2004. http://dx.doi.org/10.1007/978-1-4471-0231-1_1.

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Johnstone, David. "On the Interpretation of Hypothesis Tests following Neyman and Pearson." In Probability and Bayesian Statistics, 267–77. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-1885-9_28.

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Oellerich, M., and B. Schneider. "Chapter 3.9. Single-Point Method and Bayesian Approach for Individualizing Theophylline Dosage." In Data Presentation / Interpretation, edited by H. Keller and Ch Trendelenburg, 403–24. Berlin, Boston: De Gruyter, 1989. http://dx.doi.org/10.1515/9783110869880-019.

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Westling, Mark F., and Larry S. Davis. "Interpretation of complex scenes using Bayesian networks." In Computer Vision — ACCV'98, 201–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63931-4_216.

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Nikolopoulos, Spiros, Georgios Th Papadopoulos, Ioannis Kompatsiaris, and Ioannis Patras. "Image Interpretation by Combining Ontologies and Bayesian Networks." In Lecture Notes in Computer Science, 307–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30448-4_39.

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Paquet, Hugo. "Bayesian strategies: probabilistic programs as generalised graphical models." In Programming Languages and Systems, 519–47. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72019-3_19.

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AbstractWe introduceBayesian strategies, a new interpretation of probabilistic programs in game semantics. This interpretation can be seen as a refinement of Bayesian networks.Bayesian strategies are based on a new form ofevent structure, with two causal dependency relations respectively modelling control flow and data flow. This gives a graphical representation for probabilistic programs which resembles the concrete representations used in modern implementations of probabilistic programming.From a theoretical viewpoint, Bayesian strategies provide a rich setting for denotational semantics. To demonstrate this we give a model for a general higher-order programming language with recursion, conditional statements, and primitives for sampling from continuous distributions and trace re-weighting. This is significant because Bayesian networks do not easily support higher-order functions or conditionals.
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Conference papers on the topic "Bayesian interpretation"

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Grendar, M. "Maximum Probability and Maximum Entropy methods: Bayesian interpretation." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 23rd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2004. http://dx.doi.org/10.1063/1.1751390.

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Mosser, L., R. Oliveira, and M. Steventon. "Probabilistic Seismic Interpretation Using Bayesian Neural Networks." In 81st EAGE Conference and Exhibition 2019. European Association of Geoscientists & Engineers, 2019. http://dx.doi.org/10.3997/2214-4609.201901510.

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Lee, Sang Wan, Yong Soo Kim, and Zeungnam Bien. "Agglomerative Fuzzy Clustering based on Bayesian Interpretation." In 2007 IEEE International Conference on Information Reuse and Integration. IEEE, 2007. http://dx.doi.org/10.1109/iri.2007.4296644.

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Keller, C. Brenhin, Blair Schoene, and Kyle M. Samperton. "A BAYESIAN APPROACH TO ZIRCON AGE INTERPRETATION." In GSA Annual Meeting in Denver, Colorado, USA - 2016. Geological Society of America, 2016. http://dx.doi.org/10.1130/abs/2016am-284893.

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Mester, Rudolf. "A Bayesian view on matching and motion estimation." In 2012 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI). IEEE, 2012. http://dx.doi.org/10.1109/ssiai.2012.6202487.

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Sampietro, D., and M. Capponi. "A Bayesian Approach to the Gravity Interpretation Problem." In NSG2020 3rd Conference on Geophysics for Mineral Exploration and Mining. European Association of Geoscientists & Engineers, 2020. http://dx.doi.org/10.3997/2214-4609.202020065.

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Sang Wan Lee, Dae-Jin Kim, Yong Soo Kim, and Zeungnam Bien. "Bayesian Interpretation of Adaptive Fuzzy Neural Network Model." In 2006 IEEE International Conference on Fuzzy Systems. IEEE, 2006. http://dx.doi.org/10.1109/fuzzy.2006.1682002.

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Ma, Rui, Qiang Xing, Jinyan Zhang, Jun Wang, and Yanjiang Wang. "Logging interpretation method based on Bayesian Optimization XGBoost." In 2022 16th IEEE International Conference on Signal Processing (ICSP). IEEE, 2022. http://dx.doi.org/10.1109/icsp56322.2022.9965325.

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Su, Che-Chun, Alan C. Bovik, and Lawrence K. Cormack. "Statistical model of color and disparity with application to Bayesian stereopsis." In 2012 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI). IEEE, 2012. http://dx.doi.org/10.1109/ssiai.2012.6202480.

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Cook, Tessa, James C. Gee, R. Nick Bryan, Jeffrey T. Duda, Po-Hao Chen, Emmanuel Botzolakis, Suyash Mohan, Andreas Rauschecker, Jeffrey Rudie, and Ilya Nasrallah. "Bayesian network interface for assisting radiology interpretation and education." In Imaging Informatics for Healthcare, Research, and Applications, edited by Jianguo Zhang and Po-Hao Chen. SPIE, 2018. http://dx.doi.org/10.1117/12.2293691.

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Reports on the topic "Bayesian interpretation"

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Carlos Torres-Verdin and Mrinal K. Sen. INTEGRATED APPROACH FOR THE PETROPHYSICAL INTERPRETATION OF POST-AND PRE-STACK 3-D SEISMIC DATA, WELL-LOG DATA, CORE DATA, GEOLOGICAL INFORMATION AND RESERVOIR PRODUCTION DATA VIA BAYESIAN STOCHASTIC INVERSION. Office of Scientific and Technical Information (OSTI), September 2004. http://dx.doi.org/10.2172/837074.

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Carlos Torres-Verdin and Mrinal K. Sen. INTEGRATED APPROACH FOR THE PETROPHYSICAL INTERPRETATION OF POST- AND PRE-STACK 3-D SEISMIC DATA, WELL-LOG DATA, CORE DATA, GEOLOGICAL INFORMATION AND RESERVOIR PRODUCTION DATA VIA BAYESIAN STOCHASTIC INVERSION. Office of Scientific and Technical Information (OSTI), March 2004. http://dx.doi.org/10.2172/825256.

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