With the rapidly growing scales of statistical problems, subset based communication-free parallel MCMC methods are a promising future for large scale Bayesian analysis. In this article, we propose a new Weierstrass sampler for parallel MCMC based on independent subsets. The new sampler approximates the full data poster…
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48 results for “MCMC samplers”
Neural network MCMC sampler maximizes proposal entropy for efficient sampling.
problem Inefficient sampling from complex probability distributions.
method Proposes a neural network MCMC sampler that maximizes proposal entropy.
result Significantly higher efficiency in various sampling tasks.
New sampler reduces MCMC complexity for Bayesian variable selection.
problem High-dimensional Bayesian variable selection with high computation complexity.
method Variable-complexity subset weighted-Tempered Gibbs Sampler (wTGS) with Rao-Blackwellized estimator.
result Variances of Rao-Blackwellized estimator are smaller than those of subset wTGS.
A new benchmark system evaluates MCMC samplers using real data.
problem The evaluation of new MCMC samplers is inadequate with common methods.
method Meta-learning approach to generate benchmark examples from data sets and models, using flexible density models.
result New insights into effective sample size and estimation efficiency of samplers.
New samplers improve MCMC efficiency in high dimensions.
problem Efficient sampling in high-dimensional problems.
method Affine invariant ensemble samplers, including derivative-free and derivative-based HMC.
result Affine invariant ensemble HMC outperforms standard HMC in high dimensions.
Improves MCMC sampling for multi-modal distributions.
problem Inefficient MCMC sampling in multi-modal posterior distributions.
method Pseudo-extended MCMC method using auxiliary variables.
result Improved MCMC sampling over Hamiltonian Monte Carlo.
Meta-learning optimizes SG-MCMC dynamics for efficient Bayesian modeling.
problem Lack of tailored SG-MCMC schemes for specific models.
method Meta-learning algorithm for automating SG-MCMC sampler design.
result Learned sampler generalizes and outperforms hand-designed ones.
Enhances sampling for complex hidden Markov models using ensemble MCMC.
problem Challenges in Bayesian inference for factorial hidden Markov models due to large latent variable space.
method Introduces ensemble MCMC with parallel tempering and genetic algorithm for efficient exploration.
result Improves sampling efficiency and mixing of existing samplers in various applications.
New method for MCMC models without perfect or sequential samplers.
problem Bayesian inference for complex models with intractable terms.
method Utilizes tractable independence model to construct unbiased estimates.
result Scalable method for high-dimensional models.
New MCMC methods map high-dimensional problems to spheres for better mixing.
problem Mixing issues in high-dimensional distributions, especially heavy-tailed ones.
method Stereographic Markov Chain Monte Carlo (MCMC) methods that map high-dimensional problems to spheres.
result Uniformly ergodic samplers for various distributions, including heavy-tailed ones, with faster convergence in higher dimensions.
JaxSGMC simplifies SG-MCMC for Bayesian deep learning.
problem Uncertainty quantification in deep learning models.
method Modular stochastic gradient MCMC in JAX.
result Facilitates trustworthy neural network predictions.
A new method de-randomizes MCMC dynamics using the Stein operator.
problem Estimating complex target distributions in Bayesian inference.
method De-randomized kernel-based particle samplers that discretize the fiber-gradient Hamiltonian flow.
result GSVGD de-randomizes complex MCMC dynamics, maintaining high sample quality.
Many recent Markov chain Monte Carlo (MCMC) samplers leverage continuous dynamics to define a transition kernel that efficiently explores a target distribution. In tandem, a focus has been on devising scalable variants that subsample the data and use stochastic gradients in place of full-data gradients in the dynamic s…
New PDMP samplers tackle variable selection in models.
problem Jointly explore model space and parameter space.
method Develop reversible jump PDMP samplers.
result New samplers mix better and are more efficient.
We study probability measures induced by set functions with constraints. Such measures arise in a variety of real-world settings, where prior knowledge, resource limitations, or other pragmatic considerations impose constraints. We consider the task of rapidly sampling from such constrained measures, and develop fast M…
Efficiently samples from projection DPPs using hit-and-run MCMC.
problem Sampling from Determinantal Point Processes (DPPs) is computationally expensive.
method Combines combinatorial geometry, linear programming, and Monte Carlo methods.
result Our sampler is more sample-efficient than previous approaches.
LSB is a new MCMC method for discrete spaces that reduces target evaluations.
problem Sampling in discrete domains with high efficiency and adaptability.
method Local self-balancing proposals, mutual information objective, self-balancing learning.
result LSB converges with fewer target evaluations compared to existing methods.
New MCMC methods improve sampling efficiency and mixing.
problem Improving sampling efficiency and mixing in MCMC methods.
method Sequential proposals in MCMC methods, including HMC and bouncy particle sampler.
result New methods improve numerical efficiency and mixing in multimodal distributions.
ChEES-HMC improves SMC samplers' efficiency and speed.
problem Efficiently sampling from complex posterior distributions.
method Incorporating ChEES-HMC into SMC samplers.
result ChEES-HMC outperforms NUTS in speed and efficiency.
PTSD improves neural samplers by combining diffusion models and PT, enhancing efficiency.
problem Efficiency and correlation issues in neural samplers compared to PT.
method Sequential training of diffusion models across temperatures, combining high-temperature models for approximate lower-temperature samples.
result Significantly improved target evaluation efficiency, outperforming diffusion-based samplers.
Metropolis Samplingstat.ME
MH sampler is a core of MCMC methods in Bayesian inference.
problem Efficient sampling from complex probability distributions.
method Metropolis-Hastings algorithm and its variants.
result Comprehensive overview of MH sampler and its applications.
New samplers improve compositional generation with diffusion models.
problem Improving compositional generation with diffusion models.
method Score-based interpretation, energy-based parameterization, Metropolis-corrected samplers.
result New samplers enable successful compositional generation across various tasks.
New sampling algorithms improve efficiency in latent Gaussian models.
problem Efficient sampling from complex target distributions.
method Combines auxiliary variables, Gibbs sampling, and Taylor expansions.
result Marginal samplers are superior in asymptotic variance, but slower in computing time.
Topic models, and more specifically the class of Latent Dirichlet Allocation (LDA), are widely used for probabilistic modeling of text. MCMC sampling from the posterior distribution is typically performed using a collapsed Gibbs sampler. We propose a parallel sparse partially collapsed Gibbs sampler and compare its spe…
A fast MCMC sampler for sparse Bayesian inference.
problem Sparse Bayesian inference problems with high computational cost.
method Asynchronous Gibbs sampler extended with data sub-sampling.
result The Markov chain admits an invariant distribution that recovers the main signal with high probability.
ABC Samplersstat.CO
ABC Samplers detail methods for sampling from ABC approximations.
problem Sampling from ABC approximations to posterior distributions.
method Rejection/importance sampling, MCMC, sequential Monte Carlo.
result Various ABC sampling methods are detailed and compared.
New MCMC method improves sampling efficiency across diverse structural models.
problem Low sampling efficiency in generic MCMC methods for specific problems.
method Adaptive Principal-Component (PC) Meta-learning Stochastic Gradient Hamiltonian Monte Carlo (APM-SGHMC) algorithm.
result Universal samplers achieve zero-shot generalization across structurally distinct models.
Combines local MCMC chains to speed up sampling.
problem Designing efficient MCMC chains that mix well over the whole state space.
method Combining parallel chains prioritized by kernel Stein discrepancy, combining samples using novel probability estimation.
result Significant speedups in sampling from multimodal distributions.
Meta-learning improves MCMC proposal efficiency.
problem Manual construction of model-specific MCMC proposals is time-consuming and model-specific.
method Parametrize MCMC proposals as neural networks and use meta-learning to generalize across models.
result Learned neural proposals accelerate inference on unseen models without model-specific training.
This study compares parallel SMC and MCMC for Bayesian deep learning, showing SMC parallel is faster.
problem Efficiently performing Bayesian deep learning with parallel computing.
method Compared sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) in parallel settings.
result Parallel SMC achieves similar convergence as a single SMC but with reduced communication time.
New method accelerates Parallel Tempering using neural samplers.
problem Challenges in sampling from high-dimensional, multimodal distributions.
method Leverages neural samplers to reduce overlap between distributions.
result Improves sample quality and reduces computational cost.
Developed a Particle-Gibbs sampler for Bayesian feature allocation models.
problem Intractable exact inference in Bayesian feature allocation models.
method Particle-Gibbs sampler for feature allocation matrix updates.
result PG sampler improves performance of feature allocation models.
New MCMC method speeds up inference for complex models.
problem Intractable Bayesian inference for large datasets and high-dimensional models.
method ADMM-type splitting strategy for MCMC sampling.
result Empirical and theoretical convergence rates established.
No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplerscs.LG
New method trains neural samplers without simulation, but fails due to mode collapse.
problem Training neural samplers without simulation.
method Time-dependent normalizing flow with Langevin preconditioning.
result Langevin preconditioning is crucial for avoiding mode collapse.
New method merges MCMC samples without distributional assumptions.
problem Efficiently merging MCMC samples from disjoint subsets.
method Diffusion generative modelling for density approximation.
result Outperforms existing methods on high-dimensional problems.
Researchers analyze inverse optimal transport, deriving theoretical and empirical insights.
problem Understanding the inverse problem of inferring cost matrices from optimal couplings.
method Formalized and analyzed using entropy-regularized optimal transport, with theoretical and empirical contributions.
result Characterization of the manifold of cross-ratio equivalent costs and derivation of an MCMC sampler.
New kernel-based method detects biased MCMC samples.
problem Detecting biased MCMC samples with standard diagnostics.
method Kernel Stein discrepancy (KSD) based on Stein's method.
result KSDs can detect convergence for various target distributions.
We consider the inverse reinforcement learning problem, that is, the problem of learning from, and then predicting or mimicking a controller based on state/action data. We propose a statistical model for such data, derived from the structure of a Markov decision process. Adopting a Bayesian approach to inference, we sh…
Improved MCMC sampling for expensive, irregular likelihoods.
problem Bayesian inference challenges with irregular, expensive likelihoods.
method Adapt subset samplers, introduce data-driven proxies, adaptive controller.
result Improved HINTS algorithm achieves best sampling error in fixed budget.
We propose a stochastic gradient Markov chain Monte Carlo (SG-MCMC) algorithm for scalable inference in mixed-membership stochastic blockmodels (MMSB). Our algorithm is based on the stochastic gradient Riemannian Langevin sampler and achieves both faster speed and higher accuracy at every iteration than the current sta…
HDT improves MCMC on graphs with history-dependent sampling.
problem Efficient sampling from target distributions on general graphs with low computational overhead.
method History-driven target (HDT) framework that replaces the original target distribution with a history-dependent one.
result Near-zero variance performance and scalability to large graphs with memory-efficient implementation.
Testing MCMC codecs.SE
Markov Chain Monte Carlo (MCMC) algorithms are a workhorse of probabilistic modeling and inference, but are difficult to debug, and are prone to silent failure if implemented naively. We outline several strategies for testing the correctness of MCMC algorithms. Specifically, we advocate writing code in a modular way, w…
Analysis of high-dimensional Continuous Time Markov Chains using the Local Bouncy Particle Samplerstat.ML
New algorithm speeds up sampling for complex statistical models.
problem Sampling parameters of high-dimensional CTMCs is challenging.
method Developed a local version of the Bouncy Particle Sampler (BPS) with exact event times.
result Algorithm achieves favorable computational efficiency for real-data scenarios.
Asynchronous MCMC speeds up sampling with elastic coupling.
problem Parallel asynchronous sampling for stochastic gradient MCMC.
method Stochastic gradient Hamiltonian Monte Carlo with elastic coupling.
result Significantly speeds up exploration of target distribution.
Novel method uses MCMC to improve approximation networks.
problem Approximating complex, intractable distributions.
method Amortized MCMC with iterative refinement of approximation network.
result Improved quality of deep generative model training.
RLMH improves adaptive MCMC by optimizing contrastive divergence reward.
problem Tuning MCMC samplers is challenging and time-consuming.
method Formulated Metropolis-Hastings as a Markov decision process and used RL to adaptively tune it.
result A novel reward function based on contrastive divergence outperforms existing ones.
Proposes an exact slice sampler for HDP and its mixture models.
problem Challenges in sampling from Hierarchical Dirichlet Process (HDP) models.
method Bayesian variable augmentation to address hierarchical nature of HDPs, resulting in a full factorization of the joint distribution suitable for slice sampling.
result Fast mixing and natural truncation of infinite measures without ad-hoc modifications.
Eryn is a versatile MCMC package for Bayesian inference.
problem Bayesian inference for parameter estimation and model selection.
method Markov Chain Monte Carlo (MCMC) algorithm integrated into a user-friendly toolbox.
result Eryn can handle a wide range of Bayesian inference problems, from simple to complex.