Reflections on Bayesian inference and Markov chain Monte Carlo
From MaRDI portal
Publication:6059418
DOI10.1002/cjs.11707MaRDI QIDQ6059418
No author found.
Publication date: 2 November 2023
Published in: Canadian Journal of Statistics (Search for Journal in Brave)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Bayesian Synthetic Likelihood
- The pseudo-marginal approach for efficient Monte Carlo computations
- The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
- On the computational complexity of high-dimensional Bayesian variable selection
- Approximate Bayesian computational methods
- Conditions for rapid mixing of parallel and simulated tempering on multimodal distributions
- Sufficient conditions for torpid mixing of parallel and simulated tempering
- Computable bounds for geometric convergence rates of Markov chains
- Weak convergence and optimal scaling of random walk Metropolis algorithms
- Population Markov chain Monte Carlo
- Honest exploration of intractable probability distributions via Markov chain Monte Carlo.
- Optimal scaling for various Metropolis-Hastings algorithms.
- Bounds on regeneration times and convergence rates for Markov chains
- Merging MCMC subposteriors through Gaussian-process approximations
- Inference from iterative simulation using multiple sequences
- Renewal theory and computable convergence rates for geometrically erdgodic Markov chains
- Partial identification of probability distributions.
- Quantitative convergence rates of Markov chains: A simple account
- Sufficient burn-in for Gibbs samplers for a hierarchical random effects model.
- Rates of convergence for Gibbs sampling for variance component models
- Convergence rate of Markov chain methods for genomic motif discovery
- Bayesian adjustment for preferential testing in estimating infection fatality rates, as motivated by the COVID-19 pandemic
- High-fidelity hurricane surge forecasting using emulation and sequential experiments
- Convergence properties of pseudo-marginal Markov chain Monte Carlo algorithms
- Complexity bounds for Markov chain Monte Carlo algorithms via diffusion limits
- An overview on Approximate Bayesian computation
- Handbook of Markov Chain Monte Carlo
- Applications of Parallel Computation to Statistical Inference
- Optimal Scaling of Discrete Approximations to Langevin Diffusions
- Constructing Summary Statistics for Approximate Bayesian Computation: Semi-Automatic Approximate Bayesian Computation
- Minorization Conditions and Convergence Rates for Markov Chain Monte Carlo
- Likelihood inflating sampling algorithm
- Emulating global climate change impacts on crop yields
- Equation of State Calculations by Fast Computing Machines
- Speeding Up MCMC by Efficient Data Subsampling
- Sequential Monte Carlo without likelihoods
- Monte Carlo sampling methods using Markov chains and their applications
- The complexity of theorem-proving procedures
This page was built for publication: Reflections on Bayesian inference and Markov chain Monte Carlo
