Everything about Variance reduction
Mathematical procedure for reducing the variance of statistical estimators
In mathematics, more specifically in the theory of Monte Carlo methods, variance reduction is a procedure used to increase the precision of the estimates obtained for a given simulation or computational effort. Every output random variable from the simulation is associated with a variance which limits the precision of the simulation results. In order to make a simulation statistically efficient, i.e., to obtain a greater precision and smaller confidence intervals for the output random variable of interest, variance reduction techniques can be used. The main variance reduction methods arecommon random numbers antithetic variates control variates importance sampling stratified sampling moment matching conditional Monte Carlo and quasi random variables Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Variance reductionA 5-minute orientation
- Skim a recent paper: Fast Decentralized Nonconvex Finite-Sum Optimization with Recursive Variance ReductionInfluential work since 2022
- Pick up a classic book: Methods for statistical data analysis of multivariate observationsThe most-reprinted book on the subject
- Read the landmark paper: Variance Reduction via Lattice RulesCited 120 times
- Go sideways: Markov chain central limit theoremA closely related topic
- Go sideways: Antithetic variatesA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Variance Reduction via Lattice Rules
Variance Reduction Analysis
Variance Reduction Using Nonreversible Langevin Samplers
Variance Reduction for Simulated Diffusions
Integrated Variance Reduction Strategies for Simulation
Stochastic Conjugate Gradient Algorithm With Variance Reduction
Variance reduction in purely random forests
Variance reduction methods for CONNFFESSIT-like simulations
🆕 Influential since 2022
Fast Decentralized Nonconvex Finite-Sum Optimization with Recursive Variance Reduction
A mini-batch stochastic conjugate gradient algorithm with variance reduction
Word embedding dimensionality reduction using dynamic variance thresholding (DyVaT)
Improved Variance Reduction Methods for Riemannian Non-Convex Optimization
Physics-Constrained Bayesian Neural Network for Bias and Variance Reduction
📚 Books worth reading
Methods for statistical data analysis of multivariate observationsR. Gnanadesikan · 1976
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
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