Nonlinear MCMC improves Bayesian machine learning sampling.
problem Sampling problems in Bayesian machine learning.
method Nonlinear MCMC technique with convergence guarantees.
result Improves sampling in Bayesian neural networks.
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48 results for “Bayesian MCMC”
Bayesian neural networks tutorial via MCMC in Python.
problem Bayesian inference for parameter estimation and uncertainty quantification in deep learning models.
method MCMC sampling methods to implement Bayesian inference, including advanced proposal distributions.
result Challenges in sampling multi-modal posterior distributions for Bayesian neural networks.
Subsampling MCMC speeds up Bayesian inference for large datasets.
problem Computational demands of MCMC for large datasets.
method Pseudo-marginal MCMC approach using data subsampling.
result Subsampling MCMC can significantly speed up MCMC methods.
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.
The book covers scalable MCMC methods for Bayesian learning.
problem Scalability issues in Bayesian learning with large datasets.
method Advanced MCMC algorithms, including stochastic gradient, non-reversible, and continuous time methods.
result Substantial advances in practical and theoretical Bayesian computation.
Paper proposes first unlearning algorithm for MCMC models.
problem Enforcing right to be forgotten in AI causes high costs for data deletion.
method Converts MCMC unlearning to explicit optimization problem, designs MCMC influence function.
result MCMC unlearning does not compromise generalizability of models.
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.
Develops Cyclical SG-MCMC for exploring multimodal posterior distributions in deep learning.
problem High-dimensional, multimodal posterior distributions in Bayesian deep learning.
method Cyclical stepsize schedule in SG-MCMC to discover and characterize modes.
result Non-asymptotic convergence of the proposed algorithm.
New algorithm tackles big data Bayesian problems with latent variables.
problem Bayesian computing for large-scale problems with missing data and dimension jumping.
method Extended stochastic gradient MCMC with latent variables.
result Highly scalable and more efficient than traditional MCMC algorithms.
Unified framework connects SG-MCMC and SVGD for scalable Bayesian sampling.
problem Highly correlated samples in SG-MCMC limit scalability.
method Wasserstein gradient flows, particle-approximate techniques.
result Unified framework allows new scalable algorithms.
Parallel Markov Chain Monte Carlo for Bayesian Hierarchical Models with Big Data, in Two Stagesstat.ME
Parallelizes Bayesian MCMC for big data, improving efficiency and speed.
problem Efficiently analyzing large Bayesian hierarchical models with big data.
method Two-stage approach: first stage estimates group-specific parameters in parallel, second stage uses stage 1 posteriors as proposals.
result Agrees with full data analysis but with increased efficiency and reduced computation times.
The paper parallelizes MCMC using random forests for big data Bayesian computation.
problem Efficiently computing Bayesian models in large datasets.
method Embed random forests into divide-and-conquer MCMC framework, using scaled subposteriors as proposal distributions.
result The approach works well across various model types, including misspecified models.
MCMC struggles with BNNs but yields useful predictive distributions.
problem Challenges in sampling from Bayesian neural networks' posterior.
method Non-converged MCMC sampling for generating posterior predictive distributions.
result Non-converged MCMC can provide accurate posterior predictive distributions.
New method reduces computational cost for Bayesian inference.
problem High computational cost in Bayesian inference.
method Coreset MCMC, a Markov chain targeting coreset posterior.
result Coreset MCMC provides higher quality posterior approximations and reduced computational cost.
New MCMC algorithm improves convergence of Bayesian regression trees.
problem Local mode stickiness and poor mixing in MCMC algorithms for Bayesian regression trees.
method Continuous-time birth-death MCMC algorithm for Bayesian regression tree models.
result The new algorithm dramatically improves convergence and mixing properties of MCMC.
New MCMC method estimates differential privacy from multiple MIAs without worst-case assumptions.
problem Bayesian estimation of differential privacy from membership inference attacks.
method Bayesian estimation via MCMC algorithm (MCMC-DP-Est).
result More cautious privacy analysis with joint estimation of MIA strengths and privacy parameter.
BiDAG R package learns and samples Bayesian network structures efficiently.
problem Efficiently learning and sampling Bayesian network structures.
method Hybrid approach combining PC algorithm, iterative order MCMC, and partition MCMC.
result BiDAG can handle both discrete and continuous data.
The paper analyzes how larger minibatch sizes in SG-MCMC lead to faster convergence.
problem Theoretical analysis of impact of minibatch size on SG-MCMC convergence rate.
method Proposes a variance-reduction technique for SG-MCMC and proves its faster convergence rate.
result The proposed variance-reduction technique leads to a faster convergence rate than standard SG-MCMC.
A new hybrid MCMC method guides MCMC with tree-based clustering for faster and more efficient inference.
problem Slow convergence of MCMC methods in posterior inference for NRM mixture models.
method Tree-guided MCMC (tgMCMC) that combines MCMC's convergence guarantees with IBHC's efficiency.
result tgMCMC provides faster convergence and better performance compared to MCMC and IBHC alone.
A new MCMC method tackles doubly intractable posterior problems.
problem Sampling from complicated distributions with doubly intractable posterior.
method Multi-armed Bandit MCMC (MABMC) algorithm.
result MABMC achieves higher average acceptance probability than existing methods.
Adaptive workflow combines fast amortized inference with MCMC for many datasets.
problem Trade-off between computational speed and sampling accuracy in Bayesian inference.
method Adaptive workflow integrating amortized inference and MCMC with principled diagnostics.
result Efficiency gains with high posterior quality on tens of thousands of datasets.
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.
Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Propertiesphysics.comp-ph
Study improves Bayesian calibration of mechanical properties using active learning and MCMC.
problem Inference of spatially varying material parameters in computational mechanics.
method Comprehensive comparative study of surrogate models and MCMC algorithms.
result Active learning strategy outperforms a priori trained models in posterior estimation.
Paper assesses adversarial robustness of MCMC and BDK methods for deep Bayesian networks.
problem Assessing adversarial robustness of deep neural networks under MCMC and BDK approximations.
method Characterizes robustness of MCMC and BDK methods to FGSM and PGD attacks.
result Full MCMC-based inference shows excellent robustness, outperforming standard point estimation.
This project compares MCMC and VI for Bayesian PMF on MovieLens.
problem Intractable posterior distribution in PMF.
method Employed MCMC and VI for Bayesian inference on MovieLens.
result VI converges faster, MCMC provides more accurate estimates.
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.
SG-MCMC achieves strong differential privacy with simple modifications.
problem Balancing privacy and utility in machine learning models.
method Integrating SG-MCMC with differential privacy techniques.
result SG-MCMC achieves strong differential privacy with careful step sizes.
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.
A novel Bayesian framework for private linear regression with MCMC.
problem Private linear regression in a distributed setting.
method Generative statistical model, MCMC algorithms, fast Bayesian estimation.
result The proposed methods provide well-rounded estimation and prediction.
A novel MCMC method clusters data faster and more accurately.
problem Efficiently clustering large datasets with unknown number of clusters.
method Master/Worker architecture for distributed MCMC inference.
result Significant improvement in clustering accuracy and speed.
Improved Bayesian computation for imaging problems using a new MCMC method.
problem Challenges in Bayesian computation for imaging inverse problems due to high dimensionality and non-smoothness.
method Introduces a new accelerated proximal MCMC method (ls SK-ROCK) that combines data augmentation and relaxation with proximal MCMC.
result The method converges faster and achieves better accuracy than state-of-the-art approaches.
Bayesian structure learning improved using GFlowNets.
problem Inferring Bayesian network structure from data.
method Using Generative Flow Networks (GFlowNets) for approximating posterior DAG distributions.
result DAG-GFlowNet provides an accurate approximation of the posterior over DAGs.
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.
The paper studies SG-MCMC with stale gradients in distributed systems.
problem The impact of stale gradients on convergence properties of SG-MCMC in distributed systems.
method Developed theoretical analysis to show that while bias and MSE depend on staleness, estimation variance remains independent of staleness.
result SG-MCMC with stale gradients can achieve linear speedup in estimation variance with more workers.
Wide Bayesian neural networks have a simple weight posterior: theory and accelerated samplingstat.ML
Wide Bayesian neural networks have a simpler weight posterior, leading to faster MCMC sampling.
problem Sampling from the posterior of wide Bayesian neural networks is challenging.
method Introducing repriorisation, a data-dependent reparameterisation that simplifies the posterior distribution.
result The repriorisation map accelerates MCMC sampling, achieving up to 50x higher effective sample size.
KSD Thinning uses KSD to thin MCMC samples efficiently.
problem Efficiently representing posterior distributions in Bayesian inference.
method KSD Thinning: retains only samples exceeding a KSD threshold.
result Established convergence and complexity tradeoffs for KSD Thinning.
ABC method uses MCMC for likelihood estimation.
problem Likelihood estimation for complex models.
method Approximate Bayesian Computation (ABC) with MCMC.
result MCMC can be seen as ABC for likelihood estimation.
A new hybrid method learns Bayesian network structures more efficiently.
problem Challenging to learn Bayesian network structures due to vast possibilities and acyclicity constraints.
method Synthesises constraint-based and score/search approaches in a novel hybrid method.
result Offers markedly superior performance in learning and sampling Bayesian network structures.
Paper proposes a method to break symmetries in Bayesian matrix factorization.
problem Symmetries in posterior distribution reduce MCMC sampling efficiency.
method Modification to Gaussian prior mean and covariance to break symmetries.
result Breaking symmetries leads to lower autocorrelation and reconstruction errors.
New Gaussian priors for neural networks improve scalability and Bayesian inference stability.
problem Scalability and stability issues in Bayesian neural network inference.
method Introduces a new Gaussian neural network prior with decreasing variance in network width, enabling stable MCMC sampling.
result The new prior enables stable MCMC sampling for Bayesian neural network inference, improving scalability and stability.
A new method optimizes particles in SG-MCMC for better approximation.
problem High correlation in SG-MCMC samples.
method Directly optimizes particles to approximate target distribution.
result Particle optimization in SG-MCMC can be seen as SVGD with momentum.
New method speeds up Bayesian inverse problem solving with neural operators.
problem Solving infinite-dimensional Bayesian inverse problems with high computational cost.
method Delayed-acceptance geometric MCMC driven by derivative-informed neural operator surrogates.
result Significant speedup in generating posterior samples (3-9 times faster).
The hierarchical Dirichlet process (HDP) has become an important Bayesian nonparametric model for grouped data, such as document collections. The HDP is used to construct a flexible mixed-membership model where the number of components is determined by the data. As for most Bayesian nonparametric models, exact posterio…
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.
SGLRW improves robustness of stochastic gradient MCMC methods.
problem Sensitivity to minibatch size and gradient noise in stochastic-gradient MCMC methods.
method Proposes Stochastic Gradient Lattice Random Walk (SGLRW) with lattice-based discretization.
result SGLRW remains stable in regimes where SGLD fails, including heavy-tailed gradient noise.
SGMCMC uses data subsampling to make MCMC scalable for large datasets.
problem Scalability of MCMC for large datasets.
method Data subsampling techniques to reduce computational cost.
result SGMCMC algorithms are more efficient than standard MCMC on benchmark examples.
Gradient-based MCMC for discrete spaces improves sampling performance.
problem Sampling in discrete spaces using traditional methods is challenging.
method Introduced new discrete Metropolis-Hastings samplers inspired by MALA, with a novel preconditioning technique.
result Demonstrated strong empirical performance across various challenging sampling problems.
ParaMonte::Python streamlines Bayesian data analysis with fast Monte Carlo and MCMC routines.
problem Efficiently sampling posterior distributions in Bayesian modeling and data science.
method Serial and MPI-parallelized Markov Chain Monte Carlo (MCMC) routines.
result Automated model calibration and uncertainty quantification in Bayesian analysis.