REVIEW 3 cited by
Gradient Importance Sampling
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Adaptive Monte Carlo schemes developed over the last years usually seek to ensure ergodicity of the sampling process in line with MCMC tradition. This poses constraints on what is possible in terms of adaptation. In the general case ergodicity can only be guaranteed if adaptation is diminished at a certain rate. Importance Sampling approaches offer a way to circumvent this limitation and design sampling algorithms that keep adapting. Here I present a gradient informed variant of SMC (and its special case Population Monte Carlo) for static problems.
Forward citations
Cited by 3 Pith papers
-
Hybrid Population Monte Carlo
HPMC is a population Monte Carlo method whose proposal locations are generated both from resampled weighted samples and from short Hamiltonian Monte Carlo runs, then combined by resampling or a weighted mixture.
-
Adaptive posterior distributions for uncertainty analysis of covariance matrices in Bayesian inversion problems for multioutput signals
ATAIS is an adaptive importance sampler that alternates between sampling nonlinear-model parameters and analytically updating the noise covariance matrix, then reweights old samples to approximate the joint posterior ...
-
A Proximal Newton Adaptive Importance Sampler
PNAIS adapts importance sampling proposals using scaled Newton proximal steps, enabling efficient estimation for targets that are not differentiable.
Discussion (0). Continue with ORCID to comment.