Auswahl der wissenschaftlichen Literatur zum Thema „Bayesian Moment Matching“
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Zeitschriftenartikel zum Thema "Bayesian Moment Matching"
Zhang, Qiong, und Yongjia Song. „Moment-Matching-Based Conjugacy Approximation for Bayesian Ranking and Selection“. ACM Transactions on Modeling and Computer Simulation 27, Nr. 4 (20.12.2017): 1–23. http://dx.doi.org/10.1145/3149013.
Der volle Inhalt der QuelleFranke, Reiner, Tae-Seok Jang und Stephen Sacht. „Moment matching versus Bayesian estimation: Backward-looking behaviour in a New-Keynesian baseline model“. North American Journal of Economics and Finance 31 (Januar 2015): 126–54. http://dx.doi.org/10.1016/j.najef.2014.11.001.
Der volle Inhalt der QuelleCao, Zhixing, und Ramon Grima. „Accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data“. Journal of The Royal Society Interface 16, Nr. 153 (03.04.2019): 20180967. http://dx.doi.org/10.1098/rsif.2018.0967.
Der volle Inhalt der QuelleNakagawa, Tomoyuki, und Shintaro Hashimoto. „On Default Priors for Robust Bayesian Estimation with Divergences“. Entropy 23, Nr. 1 (27.12.2020): 29. http://dx.doi.org/10.3390/e23010029.
Der volle Inhalt der QuelleYiu, A., R. J. B. Goudie und B. D. M. Tom. „Inference under unequal probability sampling with the Bayesian exponentially tilted empirical likelihood“. Biometrika 107, Nr. 4 (21.05.2020): 857–73. http://dx.doi.org/10.1093/biomet/asaa028.
Der volle Inhalt der QuelleDimas, Christos, Vassilis Alimisis, Nikolaos Uzunoglu und Paul P. Sotiriadis. „A Point-Matching Method of Moment with Sparse Bayesian Learning Applied and Evaluated in Dynamic Lung Electrical Impedance Tomography“. Bioengineering 8, Nr. 12 (25.11.2021): 191. http://dx.doi.org/10.3390/bioengineering8120191.
Der volle Inhalt der QuelleHeath, Anna, Ioanna Manolopoulou und Gianluca Baio. „Estimating the Expected Value of Sample Information across Different Sample Sizes Using Moment Matching and Nonlinear Regression“. Medical Decision Making 39, Nr. 4 (Mai 2019): 347–59. http://dx.doi.org/10.1177/0272989x19837983.
Der volle Inhalt der QuelleBrowning, Alexander P., Christopher Drovandi, Ian W. Turner, Adrianne L. Jenner und Matthew J. Simpson. „Efficient inference and identifiability analysis for differential equation models with random parameters“. PLOS Computational Biology 18, Nr. 11 (28.11.2022): e1010734. http://dx.doi.org/10.1371/journal.pcbi.1010734.
Der volle Inhalt der QuelleHabibi, Reza. „Conditional Beta Approximation: Two Applications“. Indonesian Journal of Mathematics and Applications 2, Nr. 1 (31.03.2024): 9–23. http://dx.doi.org/10.21776/ub.ijma.2024.002.01.2.
Der volle Inhalt der QuelleLu, Chi-Ken, und Patrick Shafto. „Conditional Deep Gaussian Processes: Empirical Bayes Hyperdata Learning“. Entropy 23, Nr. 11 (23.10.2021): 1387. http://dx.doi.org/10.3390/e23111387.
Der volle Inhalt der QuelleDissertationen zum Thema "Bayesian Moment Matching"
Heath, A. „Bayesian computations for Value of Information measures using Gaussian processes, INLA and Moment Matching“. Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10050229/.
Der volle Inhalt der QuelleVallade, Vincent. „Contributions à la résolution parallèle du problème SAT“. Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS260.
Der volle Inhalt der QuelleThis thesis presents multiple and orthogonal contributions to the improvement of the parallel resolution of the Boolean satisfiability problem (or SAT problem). An instance of the SAT problem is a propositional formula of a particular form (the conjunctive normal form is the most common) representing, in general, the variables and constraints of a real-world problem, such as multi-constraint planning, hardware and software verification or cryptography. Solving the SAT problem involves determining whether there is an assignment of variables that satisfies the formula. An algorithm capable of providing an answer to this problem is called a SAT solver. A simplified view of a SAT solver is an algorithm that will traverse the set of possible combinations of values for each variable until it finds a combination that makes the formula true (the formula is SAT). If the solver has gone through all the possible combinations without finding a solution, the formula is UNSAT. Obviously, this algorithm has an exponential complexity, indeed the SAT problem is the first problem to have been determined NP-complete. Many algorithms and heuristics have been developed to accelerate the solving capacity of this problem, mainly in a sequential context. The ubiquity of multi-core machines has encouraged considerable efforts in the parallel resolution of the SAT problem. This thesis is a continuation of these efforts. The contributions made by this thesis focus on the quality of information sharing between the different workers of a parallel SAT solver. A first contribution presents an efficient method to implement an asynchronous algorithm for reducing the size of the shared information. A second contribution combines the information extracted from the particular structure of the propositional formula with the information extracted dynamically during the resolution of the problem by the solver in order to create a filter that maximizes the quality of the shared information. Finally, a last contribution deals with the integration of a component allowing to determine in a probabilistic way the truth value of the variables allowing to make a formula satisfiable. The call of this component during the solving process allows to guide the solver more quickly towards a solution (if a solution exists)
Buchteile zum Thema "Bayesian Moment Matching"
Vallade, Vincent, Saeed Nejati, Julien Sopena, Souheib Baarir und Vijay Ganesh. „Diversifying a Parallel SAT Solver with Bayesian Moment Matching“. In Dependable Software Engineering. Theories, Tools, and Applications, 227–33. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-21213-0_14.
Der volle Inhalt der QuelleCowell*, R. G., A. P. Dawid* und P. Sebastiani**. „A Comparison of Sequential Learning Methods for Incomplete Data“. In Bayesian Statistics 5, 533–42. Oxford University PressOxford, 1996. http://dx.doi.org/10.1093/oso/9780198523567.003.0031.
Der volle Inhalt der QuelleDonovan, Therese M., und Ruth M. Mickey. „The Shark Attack Problem Revisited: MCMC with the Metropolis Algorithm“. In Bayesian Statistics for Beginners, 193–211. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198841296.003.0013.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Bayesian Moment Matching"
Li, Ximing, Changchun Li, Jinjin Chi und Jihong Ouyang. „Variance Reduction in Black-box Variational Inference by Adaptive Importance Sampling“. In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/333.
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