Relevant bibliographies by topics / Bayesian analysis

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Bayesian analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 June 2025

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Journal articles on the topic "Bayesian analysis"

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Grossman, J., and M. K. Parmar. "Bayesian analysis." Journal of Epidemiology & Community Health 53, no. 10 (October 1, 1999): 652b—653b. http://dx.doi.org/10.1136/jech.53.10.652b.

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Grunkemeier, Gary L., and Nicola Payne. "Bayesian analysis." Annals of Thoracic Surgery 74, no. 6 (December 2002): 1901–8. http://dx.doi.org/10.1016/s0003-4975(02)04535-6.

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MacKinnon, Douglas, and Martin Pavlovič. "A Bayesian analysis of hop price fluctuations." Agricultural Economics (Zemědělská ekonomika) 66, No. 12 (December 26, 2020): 519–26. http://dx.doi.org/10.17221/239/2020-agricecon.

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This paper quantifies the correlation between U.S. season average prices for hops with U.S. hop stocks and U.S. hop hectarage. The Hop Equilibrium Ratio, a measure of the supply/demand relationship for U.S. hops, was introduced. Through the Bayesian inference method, the authors used these data to calculate the effect an incremental change to one metric had on the probability of directional changes of future U.S. season average prices (SAP). Between 2010 and 2020, the dominance of proprietary varieties created unprecedented cartel-like powers offering opportunities for supply- and price-management. Research results will enable more accurate forecasting and greater price stability in the hop industry.
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Forbes, Florence, and Adrian E. Raftery. "Bayesian Morphology: Fast Unsupervised Bayesian Image Analysis." Journal of the American Statistical Association 94, no. 446 (June 1999): 555–68. http://dx.doi.org/10.1080/01621459.1999.10474150.

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Brooks, Stephen, A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin. "Bayesian Data Analysis." Statistician 45, no. 2 (1996): 266. http://dx.doi.org/10.2307/2988417.

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Halloran, M. Elizabeth, Andrew Gelman, John B. Carlin, Hal S. Stern, Donald B. Rubin, Bradley P. Carlin, and Thomas A. Louis. "Bayesian Data Analysis." Journal of the American Statistical Association 92, no. 440 (December 1997): 1640. http://dx.doi.org/10.2307/2965436.

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Dixon, Dennis O., and Richard Simon. "Bayesian Subset Analysis." Biometrics 47, no. 3 (September 1991): 871. http://dx.doi.org/10.2307/2532645.

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Fearn, T., A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin. "Bayesian Data Analysis." Biometrics 52, no. 3 (September 1996): 1160. http://dx.doi.org/10.2307/2533081.

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Katti, S. K. "Robust Bayesian Analysis." Technometrics 43, no. 4 (November 2001): 493. http://dx.doi.org/10.1198/tech.2001.s53.

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Grego, John. "Bayesian Data Analysis." Technometrics 46, no. 3 (August 2004): 363–64. http://dx.doi.org/10.1198/tech.2004.s199.

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Dissertations / Theses on the topic "Bayesian analysis"

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Abrams, Keith Rowland. "Bayesian survival analysis." Thesis, University of Liverpool, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.316744.

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In cancer research the efficacy of a new treatment is often assessed by means of a clinical trial. In such trials the outcome measure of interest is usually time to death from entry into the study. The time to intermediate events may also be of interest, for example time to the spread of the disease to other organs (metastases). Thus, cancer clinical trials can be seen to generate multi-state data, in which patients may be in anyone of a finite number of states at a particular time. The classical analysis of data from cancer clinical trials uses a survival regression model. This type of model allows for the fact that patients in the trial will have been observed for different lengths of time and for some patients the time to the event of interest will not be observed (censored). The regression structure means that a measure of treatment effect can be obtained after allowing for other important factors. Clinical trials are not conducted in isolation, but are part of an on-going learning process. In order to assess the current weight of evidence for the use of a particular treatment a Bayesian approach is necessary. Such an approach allows for the formal inclusion of prior information, either in the form of clinical expertise or the results from previous studies, into the statistical analysis. An initial Bayesian analysis, for a single non-recurrent event, can be performed using non-temporal models that consider the occurrence of events up to a specific time from entry into the study. Although these models are conceptually simple, they do not explicitly allow for censoring or covariates. In order to address both of these deficiencies a Bayesian fully parametric multiplicative intensity regression model is developed. The extra complexity of this model means that approximate integration techniques are required. Asymptotic Laplace approximations and the more computer intensive Gauss-Hermite quadrature are shown to perform well and yield virtually identical results. By adopting counting process notation the multiplicative intensity model is extended to the multi-state scenario quite easily. These models are used in the analysis of a cancer clinical trial to assess the efficacy of neutron therapy compared to standard photon therapy for patients with cancer of the pelvic region. In this trial there is prior information both in the form of clinical prior beliefs and results from previous studies. The usefulness of multi-state models is also demonstrated in the analysis of a pilot quality of life study. Bayesian multi-state models are shown to provide a coherent framework for the analysis of clinical studies, both interventionist and observational, yielding clinically meaningful summaries about the current state of knowledge concerning the disease/treatment process.
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Yuan, Lin. "Bayesian nonparametric survival analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq22253.pdf.

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Conti, Gabriella, Sylvia Frühwirth-Schnatter, James J. Heckman, and Rémi Piatek. "Bayesian exploratory factor analysis." Elsevier, 2014. http://dx.doi.org/10.1016/j.jeconom.2014.06.008.

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This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates from a high dimensional set of psychological measurements. (authors' abstract)
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Fang, Qijun. "Hierarchical Bayesian Benchmark Dose Analysis." Diss., The University of Arizona, 2014. http://hdl.handle.net/10150/316773.

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An important objective in statistical risk assessment is estimation of minimum exposure levels, called Benchmark Doses (BMDs) that induce a pre-specified Benchmark Response (BMR) in a target population. Established inferential approaches for BMD analysis typically involve one-sided, frequentist confidence limits, leading in practice to what are called Benchmark Dose Lower Limits (BMDLs). Appeal to hierarchical Bayesian modeling and credible limits for building BMDLs is far less developed, however. Indeed, for the few existing forms of Bayesian BMDs, informative prior information is seldom incorporated. Here, a new method is developed by using reparameterized quantal-response models that explicitly describe the BMD as a target parameter. This potentially improves the BMD/BMDL estimation by combining elicited prior belief with the observed data in the Bayesian hierarchy. Besides this, the large variety of candidate quantal-response models available for applying these methods, however, lead to questions of model adequacy and uncertainty. Facing this issue, the Bayesian estimation technique here is further enhanced by applying Bayesian model averaging to produce point estimates and (lower) credible bounds. Implementation is facilitated via a Monte Carlo-based adaptive Metropolis (AM) algorithm to approximate the posterior distribution. Performance of the method is evaluated via a simulation study. An example from carcinogenicity testing illustrates the calculations.
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Font, Valverde Martí. "Bayesian analysis of textual data." Doctoral thesis, Universitat Politècnica de Catalunya, 2016. http://hdl.handle.net/10803/384329.

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En esta tesis se desarrolla, siempre con el enfoque bayesiano en mente, una metodología estadística para el análisis de datos discretos en su aplicación en problemas estilometría. El análisis estadístico del estilo literario se ha utilizado para caracterizar el estilo de textos y autores, y para ayudar a resolver problemas de atribución de autoría. Estudios anteriores caracterizaron el estilo usando la longitud de las palabras, la longitud de las oraciones, y la proporción de los sustantivos, artículos, adjetivos o adverbios. Los datos que aquí se utilizan van, desde la frecuencia de frecuencias de palabras, hasta el análisis simultáneo de la frecuencia de longitud de palabra y de las palabras funcionales más frecuentes. Todos estos datos son característicos del estilo de autor y al mismo tiempo independiente del contexto en el que escribe. De esta forma, se introduce un análisis bayesiano de la frecuencia de frecuencias de palabras, que tiene una distribución en forma de J inversa con las colas superiores extraordinariamente largas. Se basa en la extensión de la metodología no bayesiana de Sichel para estos datos utilizando el modelo Poisson inversa gaussiana. Los modelos se comprueban mediante la exploración de la distribución a posteriori de los errores de Pearson y por la implementación de controles de consistencia de la distribución predictiva a posteriori. La distribución a posteriori de la inversa gausiana tiene una interpretación útil, al poder ser vista como una estimación de la distribución vocabulario del autor, de la cual se pueden obtener la riqueza y diversidad de la escritura del autor. Se propone también un análisis alternativo basado en la mixtura inversa gaussiana - poisson truncada en el cero, que se obtiene cambiando el orden de la mezcla y el truncamiento. También se propone un análisis de la heterogeneidad de estilo, que es un compromiso entre el modelo de punto de cambio, que busca un cambio repentino de estilo, y el análisi de conglomerados, que no tiene en cuenta el orden. El análisis incorpora el hecho de que partes próximas de un texto tienen más probabilidades de pertenecer al mismo autor que partes del texto más separadas. El enfoque se ilustra volviendo a revisar la atribución de autoría del Tirant lo Blanc. Para el análisis de la heterogeneidad del estilo literario se propone también un análisis estadístico que utiliza simultáneamente diferentes características estilométricas, como la longitud palabra y la frecuencia de las palabras funcionales más frecuentes. Las filas de todas tablas de contingencia se agrupan simultáneamente basandose en una mezcla finita de conjuntos de modelos multinomiales con un estilo homogéneo. Esto tiene algunas ventajas sobre las heurísticas utilizadas en el análisis de conglomerados, ya que incorpora naturalmente el tamaño del texto, la naturaleza discreta de los datos y la dependencia entre las categorías. Todo ello se ilustra a través del análisis del estilo en las obras de teatro de Shakespeare, el Quijote y el Tirant lo Blanc. Finalmente, los problemas de atribución y verificación de autoría, que se tratan normalmente por separado, son tratados de forma conjunta. Esto se hace asumiendo un escenario abierto de clasificación para el problema de la atribución, contemplando la posibilidad de que ninguno de los autores candidatos, con textos conocidos para aprendijaje, es el autor de los textos en disputa. Entonces, el problema de verificación se convierte en un caso especial de problema de atribución. El modelo multinomial bayesiano propuesto permite obtener una solución exacta y cerrada para este problema de atribución de autoría más general. El enfoque al problema de verificación se ilustra mediante la exploración de si un fallo judicial condenatorio podría haber sido escrito por el juez que lo firma o no, y el enfoque al problema de atribución se ilustra revisando el problema de la autoría de los Federalist Papers.<br>In this thesis I develop statistical methodology for analyzing discrete data to be applied to stylometry problems, always with the Bayesian approach in mind. The statistical analysis of literary style has long been used to characterize the style of texts and authors, and to help settle authorship attribution problems. Early work in the literature used word length, sentence length, and proportion of nouns, articles, adjectives or adverbs to characterize literary style. I use count data that goes from the frequency of word frequency, to the simultaneous analysis of word length counts and more frequent function words counts. All of them are characteristic features of the style of author and at the same time rather independent of the context in which he writes. Here we intrude a Bayesian Analysis of word frequency counts, that have a reverse J-shaped distribution with extraordinarily long upper tails. It is based on extending Sichel's non-Bayesian methodology for frequency count data using the inverse gaussian Poisson model. The model is checked by exploring the posterior distribution of the Pearson errors and by implementing posterior predictive consistency checks. The posterior distribution of the inverse gaussian mixing density also provides a useful interpretation, because it can be seen as an estimate of the vocabulary distribution of the author, from which measures of richness and of diversity of the author's writing can be obtained. An alternative analysis is proposed based on the inverse gaussian-zero truncated Poisson mixture model, which is obtained by switching the order of the mixing and the truncation stages. An analysis of the heterogeneity of the style of a text is proposed that strikes a compromise between change-point, that analyze sudden changes in style, and cluster analysis, that does not take order into consideration. Here an analysis is proposed that strikes a compromise by incorporating the fact that parts of the text that are close together are more likely to belong to the same author than parts of the text far apart. The approach is illustrated by revisiting the authorship attribution of Tirant lo Blanc. A statistical analysis of the heterogeneity of literary style in a set of texts that simultaneously uses different stylometric characteristics, like word length and the frequency of function words, is proposed. It clusters the rows of all contingency tables simultaneously into groups with homogeneous style based on a finite mixture of sets of multinomial models. That has some advantages over the usual heuristic cluster analysis approaches as it naturally incorporates the text size, the discrete nature of the data, and the dependence between categories. All is illustrated with the analysis of the style in plays by Shakespeare, El Quijote, and Tirant lo Blanc. Finally, authorship attribution and verification problems that are usually treated separately are treated jointly. That is done by assuming an open-set classification framework for attribution problems, contemplating the possibility that neither one of the candidate authors, with training texts known to have been written by them is the author of the disputed texts. Then the verification problem becomes a special case of attribution problems.A formal Bayesian multinomial model for this more general authorship attribution is given and a closed form solution for it is derived. The approach to the verification problem is illustrated by exploring whether a court ruling sentence could have been written by the judge that signs it or not, and the approach to the attribution problem illustrated by exploring whether a court ruling sentence could have been written by the judge that signs it or not, and the approach to the attribution problem is illustrated by revisiting the authority attribution
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Husain, Syeda Tasmine. "Bayesian analysis of longitudinal models /." Internet access available to MUN users only, 2003. http://collections.mun.ca/u?/theses,163598.

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Brink, Anton Meredith. "Bayesian analysis of contingency tables." Thesis, Imperial College London, 1997. http://hdl.handle.net/10044/1/8948.

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LaBute, Gerard Joseph. "Pseudo-Bayesian response surface analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape15/PQDD_0001/MQ34971.pdf.

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Wright, Alan. "Bayesian pathway analysis in epigenetics." Thesis, University of Plymouth, 2013. http://hdl.handle.net/10026.1/1286.

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A typical gene expression data set consists of measurements of a large number of gene expressions, on a relatively small number of subjects, classified according to two or more outcomes, for example cancer or non-cancer. The identification of associations between gene expressions and outcome is a huge multiple testing problem. Early approaches to this problem involved the application of thousands of univariate tests with corrections for multiplicity. Over the past decade, numerous studies have demonstrated that analyzing gene expression data structured into predefined gene sets can produce benefits in terms of statistical power and robustness when compared to alternative approaches. This thesis presents the results of research on gene set analysis. In particular, it examines the properties of some existing methods for the analysis of gene sets. It introduces novel Bayesian methods for gene set analysis. A distinguishing feature of these methods is that the model is specified conditionally on the expression data, whereas other methods of gene set analysis and IGA generally make inferences conditionally on the outcome. Computer simulation is used to compare three common established methods for gene set analysis. In this simulation study a new procedure for the simulation of gene expression data is introduced. The simulation studies are used to identify situations in which the established methods perform poorly. The Bayesian approaches developed in this thesis apply reversible jump Markov chain Monte Carlo (RJMCMC) techniques to model gene expression effects on phenotype. The reversible jump step in the modelling procedure allows for posterior probabilities for activeness of gene set to be produced. These mixture models reverse the generally accepted conditionality and model outcome given gene expression, which is a more intuitive assumption when modelling the pathway to phenotype. It is demonstrated that the two models proposed may be superior to the established methods studied. There is considerable scope for further development of this line of research, which is appealing in terms of the use of mixture model priors that reflect the belief that a relatively small number of genes, restricted to a small number of gene sets, are associated with the outcome.
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O'Donovan, Daniel James. "Bayesian analysis of NMR data." Thesis, University of Cambridge, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.608789.

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Books on the topic "Bayesian analysis"

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O, Berger James, ed. Bayesian analysis. Amsterdam: North-Holland, 1994.

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Insua, David Ríos, and Fabrizio Ruggeri, eds. Robust Bayesian Analysis. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-1306-2.

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Ibrahim, Joseph G., Ming-Hui Chen, and Debajyoti Sinha. Bayesian Survival Analysis. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3447-8.

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A, Waller Ray, ed. Bayesian reliability analysis. Malabar, Fla: R.E. Krieger Pub. Co., 1991.

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Andrew, Gelman, ed. Bayesian data analysis. Boca Raton, FL: Chapman & Hall/CRC, 2000.

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Andrew, Gelman, ed. Bayesian data analysis. 2nd ed. Boca Raton, Fla: Chapman & Hall/CRC, 2004.

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1961-, Chen Ming-Hui, and Sinha Debajyoti, eds. Bayesian survival analysis. New York: Springer, 2001.

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Andrew, Gelman, ed. Bayesian data analysis. London: Chapman & Hall, 1995.

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Müller, Peter, Fernando Andres Quintana, Alejandro Jara, and Tim Hanson. Bayesian Nonparametric Data Analysis. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18968-0.

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Mashhoudy, Houshang. Bayesian reliability growth analysis. [s.l.]: typescript, 1985.

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Book chapters on the topic "Bayesian analysis"

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Basu, Sanjib. "Bayesian Robustness and Bayesian Nonparametrics." In Robust Bayesian Analysis, 223–40. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-1306-2_12.

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Feit, Elea McDonnell, Fred M. Feinberg, and Peter J. Lenk. "Bayesian Analysis." In International Series in Quantitative Marketing, 493–554. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53469-5_16.

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Ogino, Shuji, and Robert B. Wilson. "Bayesian Analysis." In Molecular Pathology in Clinical Practice: Genetics, 59–70. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-87374-9_5.

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Wang, Sun-Chong. "Bayesian Analysis." In Interdisciplinary Computing in Java Programming, 195–210. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0377-4_12.

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Berger, James O. "Bayesian Analysis." In Springer Series in Statistics, 118–307. New York, NY: Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4757-4286-2_4.

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Haughton, Dominique, and Jonathan Haughton. "Bayesian Analysis." In Living Standards Analytics, 129–53. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0385-2_7.

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Ogino, Shuji, and Robert B. Wilson. "Bayesian Analysis." In Molecular Pathology in Clinical Practice, 61–72. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-33227-7_5.

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Broemeling, Lyle D. "Bayesian Analysis." In Bayesian Analysis of Time Series, 9–54. Boca Raton : CRC Press, Taylor & Francis Group, 2019.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429488443-2.

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Shahbaba, Babak. "Bayesian Analysis." In Biostatistics with R, 303–15. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1302-8_13.

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Ravishanker, Nalini, Balaji Raman, and Refik Soyer. "Bayesian Analysis." In Dynamic Time Series Models using R-INLA, 1–16. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003134039-1.

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Conference papers on the topic "Bayesian analysis"

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Subbanna, Nagesh, Aaron Xavier S, D. Pooja, Kavinya Mahesh, and Krishna Khare. "Bayesian Network Based Sentiment Analysis of Political Statements." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL), 1153–58. IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933477.

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Tao, Dacheng, Jimeng Sun, Jialie Shen, Xindong Wu, Xuelong Li, Stephen J. Maybank, and Christos Faloutsos. "Bayesian tensor analysis." In 2008 IEEE International Joint Conference on Neural Networks (IJCNN 2008 - Hong Kong). IEEE, 2008. http://dx.doi.org/10.1109/ijcnn.2008.4633981.

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DEMORTIER, LUC. "BAYESIAN REFERENCE ANALYSIS." In Proceedings of PHYSTAT05. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2006. http://dx.doi.org/10.1142/9781860948985_0003.

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Wheatland, Michael S. "Bayesian Data Analysis." In Proceedings of the 22nd Canberra International Physics Summer School. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814277327_0004.

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FITZGERALD, WJ. "BAYESIAN DATA ANALYSIS." In Sonar Signal Processing 1991. Institute of Acoustics, 2024. http://dx.doi.org/10.25144/21227.

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Aps, R., N. Sawano, S. Hamada, and M. Fetissov. "Bayesian inference in oil spill response management." In RISK ANALYSIS 2010. Southampton, UK: WIT Press, 2010. http://dx.doi.org/10.2495/risk100041.

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van Erp, N., and P. van Gelder. "Bayesian logistic regression analysis." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 32nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2013. http://dx.doi.org/10.1063/1.4819994.

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Ueda, Naonori. "Bayesian relational data analysis." In the 18th ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2339530.2339659.

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Dogandžić, Aleksandar. "Bayesian Defect Signal Analysis." In QUANTITATIVE NONDESTRUCTIVE EVALUATION. AIP, 2006. http://dx.doi.org/10.1063/1.2184584.

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Balázs, Csaba, Daniel Carter, George Alverson, Pran Nath, and Brent Nelson. "Bayesian analysis of NmSuGra." In SUSY09: 7th International Conference on Supersymmetry and the Unification of Fundamental Interactions. AIP, 2010. http://dx.doi.org/10.1063/1.3327638.

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Reports on the topic "Bayesian analysis"

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Piatek, Rémi, Gabriella Conti, James Heckman, and Sylvia Frühwirth-Schnatter. Bayesian exploratory factor analysis. Cemmap, July 2014. http://dx.doi.org/10.1920/wp.cem.2014.3014.

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Nadiga, Balasubramanya T., and Daniel Livescu. Bayesian Analysis of RANS Models. Office of Scientific and Technical Information (OSTI), June 2016. http://dx.doi.org/10.2172/1257091.

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Giacomini, Raffaella, Toru Kitagawa, and Matthew Read. Robust Bayesian Analysis for Econometrics. Federal Reserve Bank of Chicago, 2021. http://dx.doi.org/10.21033/wp-2021-11.

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Schmidt, D. M., J. S. George, and C. C. Wood. Bayesian analysis of MEG visual evoked responses. Office of Scientific and Technical Information (OSTI), April 1999. http://dx.doi.org/10.2172/334231.

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Gelfand, Alan E., and Bani K. Mallick. Bayesian Analysis of Semiparametric Proportional Hazards Models. Fort Belvoir, VA: Defense Technical Information Center, March 1994. http://dx.doi.org/10.21236/ada279394.

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Blei, David. Non-Parametric Bayesian Analysis of Heterogeneous Data. Fort Belvoir, VA: Defense Technical Information Center, March 2013. http://dx.doi.org/10.21236/ada582116.

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Carlin, Bradley P., Alan E. Gelfand, and Adrian F. Smith. Hierarchical Bayesian Analysis of Change Point Problems. Fort Belvoir, VA: Defense Technical Information Center, October 1990. http://dx.doi.org/10.21236/ada228179.

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Vang, Leng, Curtis Smith, and Steven Prescott. Implementation of a Bayesian Engine for Uncertainty Analysis. Office of Scientific and Technical Information (OSTI), August 2014. http://dx.doi.org/10.2172/1166049.

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Kim, Chang-Jin, James Morley, and Jeremy M. Piger. A Bayesian Approach to Counterfactual Analysis of Structural Change. Federal Reserve Bank of St. Louis, 2004. http://dx.doi.org/10.20955/wp.2004.014.

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Goel, Prem K. Software for Bayesian Analysis: Current Status and Additional Needs. Fort Belvoir, VA: Defense Technical Information Center, May 1987. http://dx.doi.org/10.21236/ada194780.

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