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Towards Adaptive Self-Normalized Importance Samplers
Nicola Branchini, Víctor Elvira
The self-normalized importance sampling (SNIS) estimator is a Monte Carlo estimator widely used to approximate expectations in statistical signal processing and machine learning. T…
Generalizing self-normalized importance sampling with couplings
Nicola Branchini, Víctor Elvira
An essential problem in statistics and machine learning is the estimation of expectations involving PDFs with intractable normalizing constants. The self-normalized importance samp…
Adaptive importance sampling for heavy-tailed distributions via -divergence minimization
Thomas Guilmeau, Nicola Branchini, Emilie Chouzenoux +1
Adaptive importance sampling (AIS) algorithms are widely used to approximate expectations with respect to complicated target probability distributions. When the target has heavy ta…
Optimized Auxiliary Particle Filters: adapting mixture proposals via convex optimization
Nicola Branchini, Víctor Elvira
Auxiliary particle filters (APFs) are a class of sequential Monte Carlo (SMC) methods for Bayesian inference in state-space models. In their original derivation, APFs operate in an…