Modular Markov chain Monte Carlo with application to multimodal sampling
Fuente:
arXiv
Saved in:
| Main Author: | Park, Joonha |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Estimating Monte Carlo variance from multiple Markov chains
by: Gupta, Kushagra, et al.
Published: (2020)
by: Gupta, Kushagra, et al.
Published: (2020)
Scalable simulation-based inference for implicitly defined models using a metamodel for Monte Carlo log-likelihood estimator
by: Park, Joonha
Published: (2023)
by: Park, Joonha
Published: (2023)
A tutorial on spatiotemporal partially observed Markov process models via the R package spatPomp
by: Asfaw, Kidus, et al.
Published: (2021)
by: Asfaw, Kidus, et al.
Published: (2021)
Sampling from high-dimensional, multimodal distributions using automatically tuned, tempered Hamiltonian Monte Carlo
by: Park, Joonha
Published: (2021)
by: Park, Joonha
Published: (2021)
On the fundamental limitations of multiproposal Markov chain Monte Carlo algorithms
by: Pozza, Francesco, et al.
Published: (2024)
by: Pozza, Francesco, et al.
Published: (2024)
Markov chain Monte Carlo without evaluating the target: an auxiliary variable approach
by: Yuan, Wei, et al.
Published: (2024)
by: Yuan, Wei, et al.
Published: (2024)
Identification-aware Markov chain Monte Carlo
by: Kitagawa, Toru, et al.
Published: (2025)
by: Kitagawa, Toru, et al.
Published: (2025)
A coupling-based approach to f-divergences diagnostics for Markov chain Monte Carlo
by: Corenflos, Adrien, et al.
Published: (2025)
by: Corenflos, Adrien, et al.
Published: (2025)
A novel finite-sample testing procedure for composite null hypotheses via pointwise rejection
by: Park, Joonha, et al.
Published: (2026)
by: Park, Joonha, et al.
Published: (2026)
Stereographic Markov Chain Monte Carlo
by: Yang, Jun, et al.
Published: (2022)
by: Yang, Jun, et al.
Published: (2022)
Intrinsic effective sample size for manifold-valued Markov chain Monte Carlo via kernel discrepancy
by: You, Kisung
Published: (2026)
by: You, Kisung
Published: (2026)
Reversible jump Markov chain Monte Carlo and multi-model samplers
by: Fan, Yanan, et al.
Published: (2010)
by: Fan, Yanan, et al.
Published: (2010)
Nested $\hat R$: Assessing the convergence of Markov chain Monte Carlo when running many short chains
by: Margossian, Charles C., et al.
Published: (2021)
by: Margossian, Charles C., et al.
Published: (2021)
Control of probability flow in Markov chain Monte Carlo -- Nonreversibility and lifting
by: Suwa, Hidemaro, et al.
Published: (2012)
by: Suwa, Hidemaro, et al.
Published: (2012)
Fitting multivariate Hawkes processes to interval count data with an application to terrorist activity modelling -- a particle Markov chain Monte Carlo approach
by: Lambe, Jason J., et al.
Published: (2025)
by: Lambe, Jason J., et al.
Published: (2025)
An extension to reversible jump Markov chain Monte Carlo for change point problems with heterogeneous temporal dynamics
by: Gribbin, Emily, et al.
Published: (2026)
by: Gribbin, Emily, et al.
Published: (2026)
Parallel sampling of decomposable graphs using Markov chain on junction trees
by: Elmasri, Mohamad
Published: (2022)
by: Elmasri, Mohamad
Published: (2022)
Is Gibbs sampling faster than Hamiltonian Monte Carlo on GLMs?
by: Luu, Son, et al.
Published: (2024)
by: Luu, Son, et al.
Published: (2024)
MCMC using $\textit{bouncy}$ Hamiltonian dynamics: A unifying framework for Hamiltonian Monte Carlo and piecewise deterministic Markov process samplers
by: Chin, Andrew, et al.
Published: (2024)
by: Chin, Andrew, et al.
Published: (2024)
Bayesian cross-validation by parallel Markov Chain Monte Carlo
by: Cooper, Alex, et al.
Published: (2023)
by: Cooper, Alex, et al.
Published: (2023)
Unbiased Markov Chain Monte Carlo: what, why, and how
by: Atchadé, Yves F., et al.
Published: (2024)
by: Atchadé, Yves F., et al.
Published: (2024)
Gaussian Invariant Markov Chain Monte Carlo
by: Titsias, Michalis K., et al.
Published: (2025)
by: Titsias, Michalis K., et al.
Published: (2025)
Markov Chain Monte Carlo Significance Tests
by: Howes, Michael
Published: (2023)
by: Howes, Michael
Published: (2023)
Accelerating sequential Monte Carlo with surrogate likelihoods
by: Bon, Joshua J, et al.
Published: (2020)
by: Bon, Joshua J, et al.
Published: (2020)
Chained Markov melding using divide and conquer sequential Monte Carlo
by: Liu, Yixuan, et al.
Published: (2026)
by: Liu, Yixuan, et al.
Published: (2026)
Weighted shape-constrained estimation for the autocovariance sequence from a reversible Markov chain
by: Song, Hyebin, et al.
Published: (2024)
by: Song, Hyebin, et al.
Published: (2024)
Scaling of Piecewise Deterministic Monte Carlo for Anisotropic Targets
by: Bierkens, Joris, et al.
Published: (2023)
by: Bierkens, Joris, et al.
Published: (2023)
Knots and variance ordering of sequential Monte Carlo algorithms
by: Bon, Joshua J, et al.
Published: (2025)
by: Bon, Joshua J, et al.
Published: (2025)
Sequential Monte Carlo for Cut-Bayesian Posterior Computation
by: Mathews, Joseph, et al.
Published: (2024)
by: Mathews, Joseph, et al.
Published: (2024)
A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABC
by: Llorente, F., et al.
Published: (2021)
by: Llorente, F., et al.
Published: (2021)
Bayesian penalized empirical likelihood and Markov Chain Monte Carlo sampling
by: Chang, Jinyuan, et al.
Published: (2024)
by: Chang, Jinyuan, et al.
Published: (2024)
Sample correlation adjustments for robust Multi-fidelity Monte Carlo under limited pilot sampling
by: Stanley, Michael, et al.
Published: (2026)
by: Stanley, Michael, et al.
Published: (2026)
A review of Monte Carlo-based versions of the EM algorithm
by: Ruth, William
Published: (2024)
by: Ruth, William
Published: (2024)
Performance Analysis of Monte Carlo Algorithms in Dense Subgraph Identification
by: Guo, Wanru
Published: (2024)
by: Guo, Wanru
Published: (2024)
MCBench: A Benchmark Suite for Monte Carlo Sampling Algorithms
by: Ding, Zeyu, et al.
Published: (2025)
by: Ding, Zeyu, et al.
Published: (2025)
Adaptive tuning of Hamiltonian Monte Carlo methods
by: Akhmatskaya, Elena, et al.
Published: (2025)
by: Akhmatskaya, Elena, et al.
Published: (2025)
Scalable Monte Carlo for Bayesian Learning
by: Fearnhead, Paul, et al.
Published: (2024)
by: Fearnhead, Paul, et al.
Published: (2024)
Bayesian analysis of flexible Heckman selection models using Hamiltonian Monte Carlo
by: Lim, Heeju, et al.
Published: (2025)
by: Lim, Heeju, et al.
Published: (2025)
Inference for Diffusion Processes via Controlled Sequential Monte Carlo and Splitting Schemes
by: Huang, Shu, et al.
Published: (2025)
by: Huang, Shu, et al.
Published: (2025)
An efficient Monte Carlo method for valid prior-free possibilistic statistical inference
by: Martin, Ryan
Published: (2025)
by: Martin, Ryan
Published: (2025)
Similar Items
-
Estimating Monte Carlo variance from multiple Markov chains
by: Gupta, Kushagra, et al.
Published: (2020) -
Scalable simulation-based inference for implicitly defined models using a metamodel for Monte Carlo log-likelihood estimator
by: Park, Joonha
Published: (2023) -
A tutorial on spatiotemporal partially observed Markov process models via the R package spatPomp
by: Asfaw, Kidus, et al.
Published: (2021) -
Sampling from high-dimensional, multimodal distributions using automatically tuned, tempered Hamiltonian Monte Carlo
by: Park, Joonha
Published: (2021) -
On the fundamental limitations of multiproposal Markov chain Monte Carlo algorithms
by: Pozza, Francesco, et al.
Published: (2024)