Ensemble Slice Sampling

  title={Ensemble Slice Sampling},
  author={Minas Karamanis and Florian Beutler},
Slice sampling has emerged as a powerful Markov Chain Monte Carlo algorithm that adapts to the characteristics of the target distribution with minimal hand-tuning. However, Slice Sampling’s performance is highly sensitive to the user-specified initial length scale hyperparameter and the method generally struggles with poorly scaled or strongly correlated distributions. This paper introduces Ensemble Slice Sampling (ESS), a new class of algorithms that bypasses such difficulties by adaptively… Expand
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
This study reviews recent advances in UQ methods used in deep learning and investigates the application of these methods in reinforcement learning (RL), and outlines a few important applications of UZ methods. Expand
Chemical Kinetics Bayesian Inference Toolbox (CKBIT)
A Python package with a modular approach to applying different Bayesian inference techniques for kinetic rate parameter estimation and uncertainty quantification and it interfaces with Excel for ease of data entry. Expand
Nested Sampling Methods
Nested sampling (NS) computes parameter posterior distributions and makes Bayesian model comparison computationally feasible. Its strengths are the unsupervised navigation of complex, potentiallyExpand
The effect of finite halo size on the clustering of neutral hydrogen
Post-reionisation 21cm intensity mapping experiments target the spectral line of neutral hydrogen (HI) resident in dark matter haloes. According to the halo model, these discrete haloes trace theExpand
Trouble beyond H0 and the new cosmic triangles
José Luis Bernal, Licia Verde, 3 Raul Jimenez, 3 Marc Kamionkowski, David Valcin, and Benjamin D. Wandelt 5, 6 Department of Physics and Astronomy, Johns Hopkins University, 3400 North CharlesExpand
A Latent Slice Sampling Algorithm
In this paper we introduce a new sampling algorithm which has the potential to be adopted as a universal replacement to the Metropolis--Hastings algorithm. It is related to the slice sampler, andExpand
Impact of relativistic effects on the primordial non-Gaussianity signature in the large-scale clustering of quasars
Relativistic effects in clustering observations have been shown to introduce scale-dependent corrections to the galaxy over-density field on large scales, which may hamper the detection of primordialExpand


Slice Sampling
Markov chain sampling methods that automatically adapt to characteristics of the distribution being sampled can be constructed by exploiting the principle that one can sample from a distribution byExpand
Ensemble slice sampling
  • Statistics and Computing
  • 2021
  • SIAM J. Appl. Dyn. Syst.
  • 2020
Autocorrelation analysis & convergence emcee 3.0.2 documentation, 2019
  • URL https://emcee.readthedocs.io/en/stable/tutorials/autocorr/
  • 2019
Ensemble preconditioning for Markov chain Monte Carlo simulation
Two parallel Markov chain Monte Carlo methods that propagate a collective ensemble of paths, with local covariance information calculated from neighbouring replicas, providing a practical approach to difficult anisotropic sampling problems in high dimensions. Expand
A Conceptual Introduction to Hamiltonian Monte Carlo
This review provides a comprehensive conceptual account of these theoretical foundations of Hamiltonian Monte Carlo, focusing on developing a principled intuition behind the method and its optimal implementations rather of any exhaustive rigor. Expand
Reunderstanding slice sampling as parallel MCMC
A robust slice sampler, which guarantees no repeated samples, is found as a special case of Metropolis sampling that can interact with parallel chains to efficiently sample from multi-modal densitiesExpand
  • J. Mach. Learn. Res.
  • 2014
  • J. Mach. Learn. Res.
  • 2014
Automated Factor Slice Sampling
  • Matthew M. Tibbits, Chris Groendyke, M. Haran, J. Liechty
  • Computer Science, Medicine
  • Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
  • 2014
A two-pronged approach for constructing efficient, automated MCMC algorithms is described, a generalization of the univariate slice sampler where the selection of a coordinate basis (factors) as an additional tuning parameter is treated and an approach for automatically selecting tuning parameters to construct an efficient factor slices sampler is developed. Expand