Search Results for author:
Found 8 papers, 0 papers with code
Variance reduction properties of the reparameterization trick
From this, we show that the marginal variances of the reparameterization gradient estimator are smaller than those of the score function gradient estimator.
Subsampling MCMC - An introduction for the survey statistician
The rapid development of computing power and efficient Markov Chain Monte Carlo (MCMC) simulation algorithms have revolutionized Bayesian statistics, making it a highly practical inference method in applied work.
Subsampling Sequential Monte Carlo for Static Bayesian Models
SMC sequentially updates a cloud of particles through a sequence of distributions, beginning with a distribution that is easy to sample from such as the prior and ending with the posterior distribution.
Gaussian variational approximation for high-dimensional state space models
The variational parameters to be optimized are the mean vector and the covariance matrix of the approximation.
Hamiltonian Monte Carlo with Energy Conserving Subsampling
The key insight in our article is that efficient subsampling HMC for the parameters is possible if both the dynamics and the acceptance probability are computed from the same data subsample in each complete HMC iteration.
The block-Poisson estimator for optimally tuned exact subsampling MCMC
A pseudo-marginal MCMC method is proposed that estimates the likelihood by data subsampling using a block-Poisson estimator.
Scalable MCMC for Large Data Problems using Data Subsampling and the Difference Estimator
We propose a generic Markov Chain Monte Carlo (MCMC) algorithm to speed up computations for datasets with many observations.
Speeding Up MCMC by Efficient Data Subsampling
We propose Subsampling MCMC, a Markov Chain Monte Carlo (MCMC) framework where the likelihood function for $n$ observations is estimated from a random subset of $m$ observations.
