199 related articles for article (PubMed ID: 33922040)
1. A Novel Hybrid Monte Carlo Algorithm for Sampling Path Space.
Pinski FJ
Entropy (Basel); 2021 Apr; 23(5):. PubMed ID: 33922040
[TBL] [Abstract][Full Text] [Related]
2. Variational method for estimating the rate of convergence of Markov-chain Monte Carlo algorithms.
Casey FP; Waterfall JJ; Gutenkunst RN; Myers CR; Sethna JP
Phys Rev E Stat Nonlin Soft Matter Phys; 2008 Oct; 78(4 Pt 2):046704. PubMed ID: 18999558
[TBL] [Abstract][Full Text] [Related]
3. Variational Hybrid Monte Carlo for Efficient Multi-Modal Data Sampling.
Sun S; Zhao J; Gu M; Wang S
Entropy (Basel); 2023 Mar; 25(4):. PubMed ID: 37190347
[TBL] [Abstract][Full Text] [Related]
4. Hybrid Monte Carlo implementation of the Fourier path integral algorithm.
Chakravarty C
J Chem Phys; 2005 Jul; 123(2):24104. PubMed ID: 16050738
[TBL] [Abstract][Full Text] [Related]
5. Hybrid Monte Carlo algorithm for sampling rare events in space-time histories of stochastic fields.
Margazoglou G; Biferale L; Grauer R; Jansen K; Mesterházy D; Rosenow T; Tripiccione R
Phys Rev E; 2019 May; 99(5-1):053303. PubMed ID: 31212557
[TBL] [Abstract][Full Text] [Related]
6. Non-stationary phase of the MALA algorithm.
Kuntz J; Ottobre M; Stuart AM
Stoch Partial Differ Equ; 2018; 6(3):446-499. PubMed ID: 30931236
[TBL] [Abstract][Full Text] [Related]
7. A general construction for parallelizing Metropolis-Hastings algorithms.
Calderhead B
Proc Natl Acad Sci U S A; 2014 Dec; 111(49):17408-13. PubMed ID: 25422442
[TBL] [Abstract][Full Text] [Related]
8. Hamiltonian Monte Carlo method for estimating variance components.
Arakawa A; Hayashi T; Taniguchi M; Mikawa S; Nishio M
Anim Sci J; 2021; 92(1):e13575. PubMed ID: 34227195
[TBL] [Abstract][Full Text] [Related]
9. A Monte Carlo Metropolis-Hastings algorithm for sampling from distributions with intractable normalizing constants.
Liang F; Jin IH
Neural Comput; 2013 Aug; 25(8):2199-234. PubMed ID: 23607562
[TBL] [Abstract][Full Text] [Related]
10. An Auxiliary Variable Method for Markov Chain Monte Carlo Algorithms in High Dimension.
Marnissi Y; Chouzenoux E; Benazza-Benyahia A; Pesquet JC
Entropy (Basel); 2018 Feb; 20(2):. PubMed ID: 33265201
[TBL] [Abstract][Full Text] [Related]
11. Quantum-Inspired Magnetic Hamiltonian Monte Carlo.
Mongwe WT; Mbuvha R; Marwala T
PLoS One; 2021; 16(10):e0258277. PubMed ID: 34610053
[TBL] [Abstract][Full Text] [Related]
12. Compressible generalized hybrid Monte Carlo.
Fang Y; Sanz-Serna JM; Skeel RD
J Chem Phys; 2014 May; 140(17):174108. PubMed ID: 24811626
[TBL] [Abstract][Full Text] [Related]
13. A separable shadow Hamiltonian hybrid Monte Carlo method.
Sweet CR; Hampton SS; Skeel RD; Izaguirre JA
J Chem Phys; 2009 Nov; 131(17):174106. PubMed ID: 19894997
[TBL] [Abstract][Full Text] [Related]
14. Path ensembles and path sampling in nonequilibrium stochastic systems.
Harland B; Sun SX
J Chem Phys; 2007 Sep; 127(10):104103. PubMed ID: 17867733
[TBL] [Abstract][Full Text] [Related]
15. Modeling interdependent animal movement in continuous time.
Niu M; Blackwell PG; Skarin A
Biometrics; 2016 Jun; 72(2):315-24. PubMed ID: 26812666
[TBL] [Abstract][Full Text] [Related]
16. Markov Chain Monte Carlo from Lagrangian Dynamics.
Lan S; Stathopoulos V; Shahbaba B; Girolami M
J Comput Graph Stat; 2015 Apr; 24(2):357-378. PubMed ID: 26240515
[TBL] [Abstract][Full Text] [Related]
17. Perturbation bounds for Monte Carlo within Metropolis via restricted approximations.
Medina-Aguayo F; Rudolf D; Schweizer N
Stoch Process Their Appl; 2020 Apr; 130(4):2200-2227. PubMed ID: 32255890
[TBL] [Abstract][Full Text] [Related]
18. Quantifying the uncertainty in model parameters using Gaussian process-based Markov chain Monte Carlo in cardiac electrophysiology.
Dhamala J; Arevalo HJ; Sapp J; Horácek BM; Wu KC; Trayanova NA; Wang L
Med Image Anal; 2018 Aug; 48():43-57. PubMed ID: 29843078
[TBL] [Abstract][Full Text] [Related]
19. Using Hamiltonian Monte Carlo to estimate the log-linear cognitive diagnosis model via Stan.
Jiang Z; Carter R
Behav Res Methods; 2019 Apr; 51(2):651-662. PubMed ID: 29949073
[TBL] [Abstract][Full Text] [Related]
20. Solving Equations of Motion by Using Monte Carlo Metropolis: Novel Method Via Random Paths Sampling and the Maximum Caliber Principle.
González Diaz D; Davis S; Curilef S
Entropy (Basel); 2020 Aug; 22(9):. PubMed ID: 33286685
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
