Gotowe bibliografie tematyczne / Bayesian modelling

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Gotowa bibliografia na temat „Bayesian modelling”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 9 czerwca 2025

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Artykuły w czasopismach na temat "Bayesian modelling"

Short, Thomas H. "Applied Bayesian Modelling." Technometrics 46, no. 2 (May 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 (March 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 (March 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 (August 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 (March 19, 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 (February 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 (December 2002): 554. http://dx.doi.org/10.1177/096228020201100608.

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Rozprawy doktorskie na temat "Bayesian modelling"

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 and amplitude modulations, damping, volume, timbre and inharmonicity. The occurrence of note onsets in a performance of a piece of music is controlled by an underlying tempo process and the alignment of the timings to the underlying score of the music. A variety of applications are presented for these models under differing inference constraints. Where full Bayesian inference is possible, reversible-jump Markov Chain Monte Carlo is employed to estimate the number of notes and partial frequency components in each frame of music. We also use approximate techniques such as model selection criteria and variational Bayes methods for inference in situations where computation time is limited or the amount of data to be processed is large. For the higher level score parameters, greedy search and conditional modes algorithms are found to be sufficiently accurate. We emphasize the links between the models and inference algorithms developed in this thesis with that in existing and parallel work, and demonstrate the effects of making modifications to these models both theoretically and by means of experimental results.

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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 and approximate computations for the implementation of the Bayesian paradigm, based on the properties of the class of transformation models, are discussed. Finally, relationships between distance measures (in the form of either a derivative of a Bayes mapping or an induced distance) are explored, with particular reference to the construction of sensitivity measures.

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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 originally been introduced by Hjort (1990) for applications in survival analysis. It is a prior on the space of cumulative hazard functions and it has recently been widely used as a prior on an infinite dimensional space for latent feature models. Our contribution in this thesis is to collect many diverse groups of nonparametric Bayesian tools and explore algorithms to sample from them. We also explore machinery behind the theory to apply and expose some distinguished features of these procedures. These tools can be used by practitioners in many applications.

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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. Links between the return distribution and the distribution of the logarithm of the squared returns "fill be established. The one-step ahead predictive ability of the model will be measured using log-predictive scores. Asset returns, stock indices and exchange rates will be fitted using the developed methods.

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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 on current information. Here we focus on estimating the risk premium without using the linear estimator since the linear estimate is known to be an exact expression only in certain restricted cases such as the linear exponential family. Markov chain Monte Carlo (MCMC) has become a standard tool in statistical analysis. In this thesis we show how it can be used in a Bayesian setting applied to insurance credibility theory. Using MCMC methods, we can compute the premium to cover future risks to any degree of accuracy required by simulating directly from the posterior distribution of the unknown model risk parameters and then averaging the risk premium against this distribution. This is illustrated for a special case. We then consider the problem of model uncertainty and model selection in general credibility modelling. This is necessary especially when there are several competing models which seem to adequately describe the data. Most of our model selection techniques are based on the reversible jump MCMC algorithm of Green (1995, Biometrika). Recently Brooks et al. (2003, JRSSB) have proposed several implementational improvements for the vanilla reversible jump algorithm. In this thesis we apply these methods to various model selection problems in insurance credibility theory.

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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"

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

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

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Shevchenko, Pavel V. Modelling Operational Risk Using Bayesian Inference. Berlin, Heidelberg: 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. Wiesbaden: 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. Cham: 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. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118394472.

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

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Martikainen, Janne. Application of decision-analytic modelling in health economic evaluations. Kuopio: 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. Boston: 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"

Zwanzig, Silvelyn, and Rauf Ahmad. "Bayesian Modelling." In Bayesian Inference, 5–28. Boca Raton: 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, 119–33. Cham: 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, 107–20. Cham: 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, 135–42. Cham: 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, 121–28. Cham: 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, 3–16. Cham: 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, 161–68. Cham: 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, 205–12. Cham: 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, 137–40. Cham: 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, 169–72. Cham: 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"

Cheng, Li, Feng Jiao, Dale Schuurmans, and Shaojun Wang. "Variational Bayesian image modelling." In the 22nd international conference. New York, New York, USA: 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. New York, New York, USA: 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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Arunkumar, Anjana, Shashank Ginjpalli, and Chris Bryan. "Bayesian Modelling of Alluvial Diagram Complexity." In 2021 IEEE Visualization Conference (VIS). IEEE, 2021. http://dx.doi.org/10.1109/vis49827.2021.9623282.

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Raporty organizacyjne na temat "Bayesian modelling"

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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Ng, B. Survey of Bayesian Models for Modelling of Stochastic Temporal Processes. Office of Scientific and Technical Information (OSTI), October 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, September 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 (GDP); (ii) the adjusted method allows for the incorporation of the COVID-19 period without assuming sudden changes in the modelling setup after the pandemic; and (iii) the proposed adjustment generates approximation improvements relative to standard filters or similar models with no adjustments or alternative ones, but where the specific rare observations are not known. Importantly, abstracting from any adjustment may lead to over or underestimating the gap, to too-quick gap recoveries after downturns, or too-large volatility around the median potential output estimations.

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Juden, Matthew, Tichaona Mapuwei, Till Tietz, Rachel Sarguta, Lily Medina, Audrey Prost, Macartan Humphreys, et al. Process Outcome Integration with Theory (POInT): academic report. Centre for Excellence and Development Impact and Learning (CEDIL), March 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-inclusive poverty graduation intervention in Uganda. The partner teams’ theory of change were adapted into a formal causal model, depicted as a directed acyclic graph (DAG). The DAG was specified in the statistical software R, using the CausalQueries package, having extended the package to handle large models. Using a novel prior elicitation strategy to elicit beliefs over many more parameters than has previously been possible, the partner teams’ beliefs about the nature and strength of causal links in the causal model (priors) were elicited and combined into a single set of shared prior beliefs. The model was updated on data alone as well as on data plus priors to generate posterior models under different assumptions. Finally, the prior and posterior models were queried to learn about estimates of interest, and the relative role of prior beliefs and data in the combined analysis.

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Davey, Calum, Syreen Hassan, Chris Bonell, Nancy Cartwright, Macartan Humphreys, Audrey Prost, and James Hargreaves. Gaps in Evaluation Methods for Addressing Challenging Contexts in Development. Centre for Excellence and Development Impact and Learning (CEDIL), September 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 and contexts within which work is undertaken. Using examples and case studies throughout, we outline several gaps in evaluation methods that if addressed, could allow us to learn more. First, we argue that an important gap is the failure to combine the analysis and interpretation of process and outcome data, and illustrate the benefits of doing so. We then highlight principles that could be adapted to guide the integration from two methodological frameworks from other research fields, and discuss Bayesian modelling as a potential method that could be employed. Second, we place this gap within an evaluation approach, which relies on developing “midlevel” theories, and using data from evaluations to test and refine these theories to allow for knowledge from one setting to be transported to others. Finally, we identify further gaps and the challenges that confront this evaluation approach.

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