Pages that link to "Item:Q5857155"
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The following pages link to Stochastic Gradient Markov Chain Monte Carlo (Q5857155):
Displaying 32 items.
- On performance potentials and conditional Monte Carlo for gradient estimation for Markov chains (Q1290201) (← links)
- An adaptively weighted stochastic gradient MCMC algorithm for Monte Carlo simulation and global optimization (Q2159413) (← links)
- Monte Carlo co-ordinate ascent variational inference (Q2195834) (← links)
- A fast and efficient Markov chain Monte Carlo method for market microstructure model (Q2244387) (← links)
- Markov chain block coordinate descent (Q2301127) (← links)
- Stochastic gradient Hamiltonian Monte Carlo with variance reduction for Bayesian inference (Q2320597) (← links)
- Informed sub-sampling MCMC: approximate Bayesian inference for large datasets (Q2329777) (← links)
- Control variates for stochastic gradient MCMC (Q2329787) (← links)
- Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons (Q2681129) (← links)
- Exploration of the (non-)asymptotic bias and variance of stochastic gradient Langevin dynamics (Q2834489) (← links)
- Stochastic gradient Langevin dynamics with adaptive drifts (Q3390482) (← links)
- (Q4637063) (← links)
- A Hybrid Gibbs Sampler for Edge-Preserving Tomographic Reconstruction with Uncertain View Angles (Q5052904) (← links)
- Mini-Batch Metropolis–Hastings With Reversible SGLD Proposal (Q5881093) (← links)
- CUQIpy: I. Computational uncertainty quantification for inverse problems in Python (Q6149901) (← links)
- Improving sampling accuracy of stochastic gradient MCMC methods via non-uniform subsampling of gradients (Q6160667) (← links)
- Multi-index antithetic stochastic gradient algorithm (Q6171790) (← links)
- Efficient and generalizable tuning strategies for stochastic gradient MCMC (Q6172924) (← links)
- Optimal friction matrix for underdamped Langevin sampling (Q6181262) (← links)
- Computing Bayes: from then `til now (Q6540226) (← links)
- Emerging directions in Bayesian computation (Q6540230) (← links)
- Improvements on scalable stochastic Bayesian inference methods for multivariate Hawkes process (Q6547746) (← links)
- SwISS: a scalable Markov chain Monte Carlo divide-and-conquer strategy (Q6548764) (← links)
- Hybrid unadjusted Langevin methods for high-dimensional latent variable models (Q6554220) (← links)
- A langevinized ensemble Kalman filter for large-scale dynamic learning (Q6554553) (← links)
- Gibbs sampling the posterior of neural networks (Q6561781) (← links)
- Discussion of: ``Specifying prior distributions in reliability applications'': towards new formal rules for informative prior elicitation? (Q6581566) (← links)
- Generalized Bayesian likelihood-free inference (Q6635569) (← links)
- Bounding Wasserstein Distance with Couplings (Q6651401) (← links)
- Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison (Q6651425) (← links)
- Bayesian nonparametric generative modeling of large multivariate non-Gaussian spatial fields (Q6655987) (← links)
- Contraction rate estimates of stochastic gradient kinetic Langevin integrators (Q6667313) (← links)
