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Optimal scaling of discrete approximations to Langevin diffusions

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Cited by:

  1. Mike Tsionas & Marwan Izzeldin & Lorenzo Trapani, 2019. "Bayesian estimation of large dimensional time varying VARs using copulas," Papers 1912.12527, arXiv.org.
  2. Aknouche, Abdelhakim & Dimitrakopoulos, Stefanos, 2020. "On an integer-valued stochastic intensity model for time series of counts," MPRA Paper 105406, University Library of Munich, Germany.
  3. Palczewski, Andrzej & Palczewski, Jan, 2019. "Black–Litterman model for continuous distributions," European Journal of Operational Research, Elsevier, vol. 273(2), pages 708-720.
  4. Tsionas, Mike G. & Izzeldin, Marwan & Trapani, Lorenzo, 2022. "Estimation of large dimensional time varying VARs using copulas," European Economic Review, Elsevier, vol. 141(C).
  5. Tsionas, Mike G. & Michaelides, Panayotis G., 2017. "Neglected chaos in international stock markets: Bayesian analysis of the joint return–volatility dynamical system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 95-107.
  6. Burda Martin & Maheu John M., 2013. "Bayesian adaptively updated Hamiltonian Monte Carlo with an application to high-dimensional BEKK GARCH models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(4), pages 345-372, September.
  7. Dang, Khue-Dung & Quiroz, Matias & Kohn, Robert & Tran, Minh-Ngoc & Villani, Mattias, 2019. "Hamiltonian Monte Carlo with Energy Conserving Subsampling," Working Paper Series 372, Sveriges Riksbank (Central Bank of Sweden).
  8. Arnak S. Dalalyan, 2017. "Theoretical guarantees for approximate sampling from smooth and log-concave densities," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 651-676, June.
  9. Jorge I. Figueroa-Zúñiga & Cristian L. Bayes & Víctor Leiva & Shuangzhe Liu, 2022. "Robust beta regression modeling with errors-in-variables: a Bayesian approach and numerical applications," Statistical Papers, Springer, vol. 63(3), pages 919-942, June.
  10. O. F. Christensen & J. Møller & R. P. Waagepetersen, 2001. "Geometric Ergodicity of Metropolis-Hastings Algorithms for Conditional Simulation in Generalized Linear Mixed Models," Methodology and Computing in Applied Probability, Springer, vol. 3(3), pages 309-327, September.
  11. Bédard, Mylène, 2017. "Hierarchical models: Local proposal variances for RWM-within-Gibbs and MALA-within-Gibbs," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 231-246.
  12. Rishikesh Yadav & Raphaël Huser & Thomas Opitz, 2021. "Spatial hierarchical modeling of threshold exceedances using rate mixtures," Environmetrics, John Wiley & Sons, Ltd., vol. 32(3), May.
  13. Jure Vogrinc & Samuel Livingstone & Giacomo Zanella, 2023. "Optimal design of the Barker proposal and other locally balanced Metropolis–Hastings algorithms," Biometrika, Biometrika Trust, vol. 110(3), pages 579-595.
  14. N. Englezos & X. Kartala & P. Koundouri & M. Tsionas & A. Alamanos, 2023. "A Novel HydroEconomic - Econometric Approach for Integrated Transboundary Water Management Under Uncertainty," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 84(4), pages 975-1030, April.
  15. Kamatani, Kengo, 2020. "Random walk Metropolis algorithm in high dimension with non-Gaussian target distributions," Stochastic Processes and their Applications, Elsevier, vol. 130(1), pages 297-327.
  16. Xiang, Fei & Neal, Peter, 2014. "Efficient MCMC for temporal epidemics via parameter reduction," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 240-250.
  17. Jean-Jacques Forneron, 2022. "Estimation and Inference by Stochastic Optimization," Papers 2205.03254, arXiv.org.
  18. G. O. Roberts & O. Stramer, 2002. "Langevin Diffusions and Metropolis-Hastings Algorithms," Methodology and Computing in Applied Probability, Springer, vol. 4(4), pages 337-357, December.
  19. Quan Zhou & Jun Yang & Dootika Vats & Gareth O. Roberts & Jeffrey S. Rosenthal, 2022. "Dimension‐free mixing for high‐dimensional Bayesian variable selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1751-1784, November.
  20. Vandecasteele, Hannes & Samaey, Giovanni, 2024. "Computational efficiency study of a micro-macro Markov chain Monte Carlo method for molecular dynamics," Applied Mathematics and Computation, Elsevier, vol. 474(C).
  21. Gael M. Martin & David T. Frazier & Christian P. Robert, 2022. "Computing Bayes: From Then `Til Now," Monash Econometrics and Business Statistics Working Papers 14/22, Monash University, Department of Econometrics and Business Statistics.
  22. Shao, Wei & Guo, Guangbao & Meng, Fanyu & Jia, Shuqin, 2013. "An efficient proposal distribution for Metropolis–Hastings using a B-splines technique," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 465-478.
  23. Yang, Jun & Roberts, Gareth O. & Rosenthal, Jeffrey S., 2020. "Optimal scaling of random-walk metropolis algorithms on general target distributions," Stochastic Processes and their Applications, Elsevier, vol. 130(10), pages 6094-6132.
  24. Moffa, Giusi & Kuipers, Jack, 2014. "Sequential Monte Carlo EM for multivariate probit models," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 252-272.
  25. Lambert, Philippe & Eilers, Paul H.C., 2009. "Bayesian density estimation from grouped continuous data," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1388-1399, February.
  26. Crespo, Marelys, 2024. "Discretisation of Langevin diffusion in the weak log-concave case," TSE Working Papers 24-1506, Toulouse School of Economics (TSE).
  27. Mattingly, J. C. & Stuart, A. M. & Higham, D. J., 2002. "Ergodicity for SDEs and approximations: locally Lipschitz vector fields and degenerate noise," Stochastic Processes and their Applications, Elsevier, vol. 101(2), pages 185-232, October.
  28. M Ludkin & C Sherlock, 2023. "Hug and hop: a discrete-time, nonreversible Markov chain Monte Carlo algorithm," Biometrika, Biometrika Trust, vol. 110(2), pages 301-318.
  29. Mamatzakis, Emmanuel C. & Tsionas, Mike G., 2021. "Making inference of British household's happiness efficiency: A Bayesian latent model," European Journal of Operational Research, Elsevier, vol. 294(1), pages 312-326.
  30. Beskos, A. & Pinski, F.J. & Sanz-Serna, J.M. & Stuart, A.M., 2011. "Hybrid Monte Carlo on Hilbert spaces," Stochastic Processes and their Applications, Elsevier, vol. 121(10), pages 2201-2230, October.
  31. Gael M. Martin & David T. Frazier & Christian P. Robert, 2020. "Computing Bayes: Bayesian Computation from 1763 to the 21st Century," Monash Econometrics and Business Statistics Working Papers 14/20, Monash University, Department of Econometrics and Business Statistics.
  32. Zanella, Giacomo & Bédard, Mylène & Kendall, Wilfrid S., 2017. "A Dirichlet form approach to MCMC optimal scaling," Stochastic Processes and their Applications, Elsevier, vol. 127(12), pages 4053-4082.
  33. Anandamayee Majumdar & Corinna Gries & Jason Walker, 2011. "A non-stationary spatial generalized linear mixed model approach for studying plant diversity," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(9), pages 1935-1950, October.
  34. Xifara, T. & Sherlock, C. & Livingstone, S. & Byrne, S. & Girolami, M., 2014. "Langevin diffusions and the Metropolis-adjusted Langevin algorithm," Statistics & Probability Letters, Elsevier, vol. 91(C), pages 14-19.
  35. Samuel Livingstone & Giacomo Zanella, 2022. "The Barker proposal: Combining robustness and efficiency in gradient‐based MCMC," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 496-523, April.
  36. Dalalyan, Arnak S. & Karagulyan, Avetik, 2019. "User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient," Stochastic Processes and their Applications, Elsevier, vol. 129(12), pages 5278-5311.
  37. Martin Burda & John Maheu, 2011. "Bayesian Adaptive Hamiltonian Monte Carlo with an Application to High-Dimensional BEKK GARCH Models," Working Papers tecipa-438, University of Toronto, Department of Economics.
  38. Panayotis Michaelides & Mike Tsionas & Panos Xidonas, 2020. "A Bayesian Signals Approach for the Detection of Crises," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 18(3), pages 551-585, September.
  39. Peter Neal & Gareth Roberts, 2008. "Optimal Scaling for Random Walk Metropolis on Spherically Constrained Target Densities," Methodology and Computing in Applied Probability, Springer, vol. 10(2), pages 277-297, June.
  40. Delis, Manthos D. & Tsionas, Mike G., 2018. "Measuring management practices," International Journal of Production Economics, Elsevier, vol. 199(C), pages 65-77.
  41. Schyan Zafar & Geoff K. Nicholls, 2022. "Measuring diachronic sense change: New models and Monte Carlo methods for Bayesian inference," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1569-1604, November.
  42. Hammer, Hugo & Tjelmeland, Håkon, 2011. "Approximate forward-backward algorithm for a switching linear Gaussian model," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 154-167, January.
  43. Reihaneh Entezari & Patrick E. Brown & Jeffrey S. Rosenthal, 2020. "Bayesian spatial analysis of hardwood tree counts in forests via MCMC," Environmetrics, John Wiley & Sons, Ltd., vol. 31(4), June.
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