Adaptive multiple importance sampling for Gaussian processes
From MaRDI portal
Publication:5106877
DOI10.1080/00949655.2017.1280037OpenAlexW2229155352MaRDI QIDQ5106877
Xiaoyu Xiong, Václav Šmídl, Maurizio Filippone
Publication date: 22 April 2020
Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1508.01050
Related Items (2)
Consistency of adaptive importance sampling and recycling schemes ⋮ Altering Gaussian process to Student-t process for maximum distribution construction
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- The pseudo-marginal approach for efficient Monte Carlo computations
- The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
- Adaptive proposal distribution for random walk Metropolis algorithm
- A comparative evaluation of stochastic-based inference methods for Gaussian process models
- A general framework for the parametrization of hierarchical models
- On some properties of Markov chain Monte Carlo simulation methods based on the particle filter
- Markov chain Monte Carlo: can we trust the third significant figure?
- Convergence of adaptive mixtures of importance sampling schemes
- Efficient global optimization of expensive black-box functions
- Weak convergence and optimal scaling of random walk Metropolis algorithms
- Slice sampling. (With discussions and rejoinder)
- Inference from iterative simulation using multiple sequences
- Recursive nonlinear estimation. A geometric approach
- Probabilistic prediction of neurological disorders with a statistical assessment of neuroimaging data modalities
- Consistency of adaptive importance sampling and recycling schemes
- Optimal tuning of the hybrid Monte Carlo algorithm
- Bayesian Calibration of Computer Models
- Adaptive Multiple Importance Sampling
- Safe and Effective Importance Sampling
- Equation of State Calculations by Fast Computing Machines
- INLA or MCMC? A tutorial and comparative evaluation for spatial prediction in log-Gaussian Cox processes
- Adaptive importance sampling in monte carlo integration
- Minimum variance importance samplingviaPopulation Monte Carlo
- Monte Carlo sampling methods using Markov chains and their applications
- An adaptive Metropolis algorithm
This page was built for publication: Adaptive multiple importance sampling for Gaussian processes
