Bibliografie tematiche / Bayesian modelling

Letteratura scientifica selezionata sul tema "Bayesian modelling"

Autore: Grafiati
Pubblicato: 4 giugno 2021
Ultima modifica: 11 marzo 2023

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Articoli di riviste sul tema "Bayesian modelling"

1

Short, Thomas H. "Applied Bayesian Modelling". Technometrics 46, n. 2 (maggio 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, n. 461 (marzo 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, n. 469 (marzo 2005): 355–56. http://dx.doi.org/10.1198/jasa.2005.s12.

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Ganocy, Stephen J. "Bayesian Statistical Modelling". Technometrics 44, n. 3 (agosto 2002): 291–92. http://dx.doi.org/10.1198/004017002320256495.

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Congdon, Peter. "Bayesian Statistical Modelling". Measurement Science and Technology 13, n. 4 (19 marzo 2002): 643. http://dx.doi.org/10.1088/0957-0233/13/4/703.

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Gunn, Roger, V. Schmid, B. Whitcher e V. Cunningham. "Bayesian kinetic modelling". NeuroImage 31 (gennaio 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 e D. J. Hand. "Bayesian partition modelling". Computational Statistics & Data Analysis 38, n. 4 (febbraio 2002): 475–85. http://dx.doi.org/10.1016/s0167-9473(01)00073-1.

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Svensén, Markus, e Christopher M. Bishop. "Robust Bayesian mixture modelling". Neurocomputing 64 (marzo 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 e T. D. Lee. "Bayesian Modelling and Sensitivity Analysis". Statistician 35, n. 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, n. 6 (dicembre 2002): 554. http://dx.doi.org/10.1177/096228020201100608.

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Tesi sul tema "Bayesian modelling"

1

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 20th 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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Libri sul tema "Bayesian modelling"

1

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

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Bayesian statistical modelling. 2a ed. Chichester, England: John Wiley & Sons, 2006.

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3

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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Modelling Operational Risk Using Bayesian Inference. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

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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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Alston, Clair L., Kerrie L. Mengersen e Anthony N. Pettitt, a cura di. 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, a cura di. Modelling uncertain data. Berlin: Akademie Verlag, 1992.

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1957-, Clark James Samuel, e Gelfand Alan E. 1945-, a cura di. Hierarchical modelling for the environmental sciences: Statistical methods and applications. Oxford: Oxford University Press, 2006.

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

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Capitoli di libri sul tema "Bayesian modelling"

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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 (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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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. "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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Ferreira, João Filipe, e Jorge Dias. "Bayesian Programming and Modelling". In Springer Tracts in Advanced Robotics, 71–102. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02006-8_3.

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Shevchenko, Pavel V. "Modelling Dependence". In Modelling Operational Risk Using Bayesian Inference, 235–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15923-7_7.

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Clifford, Samuel, e Samantha Low Choy. "Bayesian Splines". In Case Studies in Bayesian Statistical Modelling and Analysis, 197–220. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118394472.ch12.

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Shevchenko, Pavel V. "Modelling Large Losses". In Modelling Operational Risk Using Bayesian Inference, 203–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15923-7_6.

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Fasiolo, Matteo, e Simon N. Wood. "ABC in Ecological Modelling". In Handbook of Approximate Bayesian Computation, 597–622. Boca Raton, Florida : CRC Press, [2019]: Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315117195-20.

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Atti di convegni sul tema "Bayesian modelling"

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Cheng, Li, Feng Jiao, Dale Schuurmans e 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, e 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 e 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 e 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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Gunning, J., e 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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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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Arunkumar, Anjana, Shashank Ginjpalli e 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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Rapporti di organizzazioni sul tema "Bayesian modelling"

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Ng, B. Survey of Bayesian Models for Modelling of Stochastic Temporal Processes. Office of Scientific and Technical Information (OSTI), ottobre 2006. http://dx.doi.org/10.2172/900168.

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