Gotowe bibliografie tematyczne / Bayesian modelling

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Gotowa bibliografia na temat „Bayesian modelling”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 25 lipca 2025

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Artykuły w czasopismach na temat "Bayesian modelling"

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Short, Thomas H. "Applied Bayesian Modelling." Technometrics 46, no. 2 (2004): 249–50. http://dx.doi.org/10.1198/004017004000000293.

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Cowles, Mary Kathryn. "Bayesian Statistical Modelling." Journal of the American Statistical Association 98, no. 461 (2003): 256–57. http://dx.doi.org/10.1198/jasa.2003.s262.

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Müller, Peter. "Applied Bayesian Modelling." Journal of the American Statistical Association 100, no. 469 (2005): 355–56. http://dx.doi.org/10.1198/jasa.2005.s12.

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Ganocy, Stephen J. "Bayesian Statistical Modelling." Technometrics 44, no. 3 (2002): 291–92. http://dx.doi.org/10.1198/004017002320256495.

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Congdon, Peter. "Bayesian Statistical Modelling." Measurement Science and Technology 13, no. 4 (2002): 643. http://dx.doi.org/10.1088/0957-0233/13/4/703.

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Gunn, Roger, V. Schmid, B. Whitcher, and V. Cunningham. "Bayesian kinetic modelling." NeuroImage 31 (January 2006): T71. http://dx.doi.org/10.1016/j.neuroimage.2006.04.061.

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Denison, D. G. T., N. M. Adams, C. C. Holmes, and D. J. Hand. "Bayesian partition modelling." Computational Statistics & Data Analysis 38, no. 4 (2002): 475–85. http://dx.doi.org/10.1016/s0167-9473(01)00073-1.

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Svensén, Markus, and Christopher M. Bishop. "Robust Bayesian mixture modelling." Neurocomputing 64 (March 2005): 235–52. http://dx.doi.org/10.1016/j.neucom.2004.11.018.

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Skene, A. M., J. E. H. Shaw, and T. D. Lee. "Bayesian Modelling and Sensitivity Analysis." Statistician 35, no. 2 (1986): 281. http://dx.doi.org/10.2307/2987533.

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Pettit, Lawrence. "Book Review: Bayesian statistical modelling." Statistical Methods in Medical Research 11, no. 6 (2002): 554. http://dx.doi.org/10.1177/096228020201100608.

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Rozprawy doktorskie na temat "Bayesian modelling"

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Peeling, Paul Halliday. "Bayesian methods in music modelling." Thesis, University of Cambridge, 2011. https://www.repository.cam.ac.uk/handle/1810/237236.

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This thesis presents several hierarchical generative Bayesian models of musical signals designed to improve the accuracy of existing multiple pitch detection systems and other musical signal processing applications whilst remaining feasible for real-time computation. At the lowest level the signal is modelled as a set of overlapping sinusoidal basis functions. The parameters of these basis functions are built into a prior framework based on principles known from musical theory and the physics of musical instruments. The model of a musical note optionally includes phenomena such as frequency an
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Strimenopoulou, Foteini. "Bayesian modelling of functional data." Thesis, University of Kent, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.544037.

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Polson, Nicholas G. "Bayesian perspectives on statistical modelling." Thesis, University of Nottingham, 1988. http://eprints.nottingham.ac.uk/11292/.

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This thesis explores the representation of probability measures in a coherent Bayesian modelling framework, together with the ensuing characterisation properties of posterior functionals. First, a decision theoretic approach is adopted to provide a unified modelling criterion applicable to assessing prior-likelihood combinations, design matrices, model dimensionality and choice of sample size. The utility structure and associated Bayes risk induces a distance measure, introducing concepts from differential geometry to aid in the interpretation of modelling characteristics. Secondly, analytical
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Baker, Peter John. "Applied Bayesian modelling in genetics." Thesis, Queensland University of Technology, 2001.

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Habli, Nada. "Nonparametric Bayesian Modelling in Machine Learning." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34267.

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Nonparametric Bayesian inference has widespread applications in statistics and machine learning. In this thesis, we examine the most popular priors used in Bayesian non-parametric inference. The Dirichlet process and its extensions are priors on an infinite-dimensional space. Originally introduced by Ferguson (1983), its conjugacy property allows a tractable posterior inference which has lately given rise to a significant developments in applications related to machine learning. Another yet widespread prior used in nonparametric Bayesian inference is the Beta process and its extensions. It has
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Delatola, Eleni-Ioanna. "Bayesian nonparametric modelling of financial data." Thesis, University of Kent, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.589934.

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This thesis presents a class of discrete time univariate stochastic volatility models using Bayesian nonparametric techniques. In particular, the models that will be introduced are not only the basic stochastic volatility model, but also the heavy-tailed model using scale mixture of Normals and the leverage model. The aim will be focused on capturing flexibly the distribution of the logarithm of the squared return under the aforementioned models using infinite mixture of Normals. Parameter estimates for these models will be obtained using Markov chain Monte Carlo methods and the Kalman filter.
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Yan, Haojie. "Bayesian spatial modelling of air pollution." Thesis, University of Bath, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.541668.

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Brown, G. O. "Model discrimination in Bayesian credibility modelling." Thesis, University of Cambridge, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.596996.

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This thesis is about insurance models and aspects of uncertainty pertaining to such models. The models we consider are insurance credibility models, arising from the need for accurate rate making based on past experience of claims in some portfolio of insurance policies. Classical credibility modelling is concerned with the use of a linear estimate to approximate the risk premium and was first studied by American actuaries at the start of the 20<sup>th</sup> century. In the Bayesian paradigm the credibility premium is the optimal linear premium since it minimises the expected square loss based
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Kheradmandnia, Manouchehr. "Aspects of Bayesian threshold autoregressive modelling." Thesis, University of Kent, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303040.

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Smith, Elizabeth. "Bayesian modelling of extreme rainfall data." Thesis, University of Newcastle Upon Tyne, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.424142.

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Książki na temat "Bayesian modelling"

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Congdon, Peter. Applied Bayesian Modelling. John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118895047.

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yliopisto, Oulun, ed. On nonparametric Bayesian hierarchical modelling. Oulun Yliopisto, 1996.

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Shevchenko, Pavel V. Modelling Operational Risk Using Bayesian Inference. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-15923-7.

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Stübler, Sabine. Modelling Proteasome Dynamics in a Bayesian Framework. Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-20167-8.

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Omre, Henning, Torstein M. Fjeldstad, and Ole Bernhard Forberg. Bayesian Spatial Modelling with Conjugate Prior Models. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65418-3.

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Alston, Clair L., Kerrie L. Mengersen, and Anthony N. Pettitt, eds. Case Studies in Bayesian Statistical Modelling and Analysis. John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118394472.

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Hans, Bandemer, ed. Modelling uncertain data. Akademie Verlag, 1992.

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Martikainen, Janne. Application of decision-analytic modelling in health economic evaluations. University of Kuopio, 2008.

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Frontis Workshop on Bayesian Statistics and Quality Modelling in the Agro-food Production Chain (2003 Wageningen, Netherlands). Bayesian statistics and quality modelling in the agro-food production chain: Proceedings of the Frontis Workshop on Bayesian Statistics and Quality Modelling in the Agro-food Production Chain, Wageningen, The Netherlands, 11-14 May 2003. Kluwer Academic, 2004.

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Congdon, Peter. Applied Bayesian Modelling. Wiley & Sons, Incorporated, John, 2003.

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Części książek na temat "Bayesian modelling"

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Zwanzig, Silvelyn, and Rauf Ahmad. "Bayesian Modelling." In Bayesian Inference. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003221623-2.

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van Oijen, Marcel. "Graphical Modelling." In Bayesian Compendium. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-66085-6_16.

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van Oijen, Marcel. "Graphical Modelling (GM)." In Bayesian Compendium. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55897-0_15.

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van Oijen, Marcel. "Bayesian Hierarchical Modelling." In Bayesian Compendium. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-66085-6_17.

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van Oijen, Marcel. "Bayesian Hierarchical Modelling (BHM)." In Bayesian Compendium. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55897-0_16.

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Omre, Henning, Torstein M. Fjeldstad, and Ole Bernhard Forberg. "Bayesian Spatial Modelling." In Bayesian Spatial Modelling with Conjugate Prior Models. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65418-3_2.

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van Oijen, Marcel. "Spatial Modelling and Scaling Error." In Bayesian Compendium. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55897-0_22.

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van Oijen, Marcel. "Spatial Modelling and Scaling Error." In Bayesian Compendium. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-66085-6_23.

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van Oijen, Marcel. "Linear Modelling: LM, , and Mixed Models." In Bayesian Compendium. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55897-0_19.

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van Oijen, Marcel. "Spatio-Temporal Modelling and Adaptive Sampling." In Bayesian Compendium. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55897-0_23.

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Streszczenia konferencji na temat "Bayesian modelling"

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T�nnis, Stefan, Luise F. Kaven, and Eike Cramer. "Comparison of Multi-Fidelity Modelling Methods for Bayesian Optimization." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.198776.

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In process systems engineering (PSE), obtaining accurate process models for optimization can be expensive and time-consuming. Black-box Bayesian Optimization (BO) with Gaussian process (GP) surrogates offers a promising approach. However, full black-box optimization neglects valuable prior knowledge, which could otherwise improve the optimization process. This work explores methods of integrating prior knowledge in the form of low-fidelity data into BO by evaluating these methods on synthetic multi-fidelity test functions. Our results highlight possibilities for improved convergence of the BO
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Cheng, Li, Feng Jiao, Dale Schuurmans, and Shaojun Wang. "Variational Bayesian image modelling." In the 22nd international conference. ACM Press, 2005. http://dx.doi.org/10.1145/1102351.1102368.

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Ridgway, Gerard, and Simon Godsill. "Bayesian modelling of microarray images." In 2006 IEEE International Workshop on Genomic Signal Processing and Statistics. IEEE, 2006. http://dx.doi.org/10.1109/gensips.2006.353146.

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"Hierarchical Bayesian Modelling of Visual Attention." In International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004731303470358.

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Kumar, Anil, Rohit Kumar Shrivastava, and Kumar Hemant Singh. "Bayesian modelling for determining material properties." In 2018 International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering (ICRIEECE). IEEE, 2018. http://dx.doi.org/10.1109/icrieece44171.2018.9009196.

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Kočková, E., A. Kučerová, and J. Sýkora. "UNCERTAINTY QUANTIFICATION THROUGH BAYESIAN NONPARAMETRIC MODELLING." In Engineering Mechanics 2020. Institute of Thermomechanics of the Czech Academy of Sciences, Prague, 2020. http://dx.doi.org/10.21495/5896-3-274.

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"Bayesian hierarchical modelling of rainfall extremes." In 20th International Congress on Modelling and Simulation (MODSIM2013). Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2013. http://dx.doi.org/10.36334/modsim.2013.l12.lehmann.

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Fitzgerald, W. J. "The Bayesian approach to signal modelling." In IEE Colloquium on Non-Linear Signal and Image Processing. IEE, 1998. http://dx.doi.org/10.1049/ic:19980444.

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Gallagher, Ian. "Bayesian block modelling for weighted networks." In the Eighth Workshop. ACM Press, 2010. http://dx.doi.org/10.1145/1830252.1830260.

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Gunning, J., and M. E. Glinsky. "Error Modelling in Bayesian CSEM Inversion." In 72nd EAGE Conference and Exhibition incorporating SPE EUROPEC 2010. European Association of Geoscientists & Engineers, 2010. http://dx.doi.org/10.3997/2214-4609.201400737.

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Raporty organizacyjne na temat "Bayesian modelling"

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Savisaar, Rosina. Introduction to Bayesian Statistical Modelling. Instats Inc., 2024. http://dx.doi.org/10.61700/xmn5u1mrxq79j1507.

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This 5-day workshop provides an intuitive hands-on introduction to statistical modelling, viewed from the Bayesian perspective. The course starts by covering the very basics of what a model is, building up to fairly sophisticated models by the last session (for example, predicting COVID-19 outcomes from biomarker data). An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers ECTS Equivalent points.
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Slater, Justin. Introduction to Bayesian Modelling using RStan (Free Seminar). Instats Inc., 2025. https://doi.org/10.61700/1falmp59ogop21675.

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This seminar provides a comprehensive introduction to Bayesian analysis using R and RStan, with applications in biostatistics and epidemiology. Participants will learn to formulate and implement custom Bayesian models, building their autonomy when applying these methods to their own statistical problems.
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Ng, B. Survey of Bayesian Models for Modelling of Stochastic Temporal Processes. Office of Scientific and Technical Information (OSTI), 2006. http://dx.doi.org/10.2172/900168.

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Granado, Camilo, and Daniel Parra-Amado. Estimating the Output Gap After COVID: How to Address Unprecedented Macroeconomic Variations. Banco de la República, 2023. http://dx.doi.org/10.32468/be.1249.

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This study examines whether and how important it is to adjust output gap frameworks during the COVID-19 pandemic and similar unprecedentedly large-scale episodes. Our proposed modelling framework comprises a Bayesian Structural Vector Autoregressions with an identification setup based on a permanent-transitory decomposition that exploits the long-run relationship of consumption with output and whose residuals are scaled up around the COVID-19 period. Our results indicate that (i) a single structural error is usually sufficient to explain the permanent component of the gross domestic product (G
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Juden, Matthew, Tichaona Mapuwei, Till Tietz, et al. Process Outcome Integration with Theory (POInT): academic report. Centre for Excellence and Development Impact and Learning (CEDIL), 2023. http://dx.doi.org/10.51744/crpp5.

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This paper describes the development and testing of a novel approach to evaluating development interventions – the POInT approach. The authors used Bayesian causal modelling to integrate process and outcome data to generate insights about all aspects of the theory of change, including outcomes, mechanisms, mediators and moderators. They partnered with two teams who had evaluated or were evaluating complex development interventions: The UPAVAN team had evaluated a nutrition-sensitive agriculture intervention in Odisha, India, and the DIG team was in the process of evaluating a disability-inclus
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Davey, Calum, Syreen Hassan, Chris Bonell, et al. Gaps in Evaluation Methods for Addressing Challenging Contexts in Development. Centre for Excellence and Development Impact and Learning (CEDIL), 2017. http://dx.doi.org/10.51744/cpip4.

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We start this paper by emphasizing that that we currently do not learn as much as we could from evaluations. While there are well-established methods for determining, and understanding, the effects of simpler interventions in one set of places (i.e. internal validity), it is less clear how to learn the most possible from evaluations of context-specific, complex, interventions, and apply what we learn to other contexts. This is especially important in international development where evaluations are limited by time, cost and opportunity, and where there is significant heterogeneity in the issues
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