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Found 6 papers, 3 papers with code
Constructing unbiased gradient estimators with finite variance for conditional stochastic optimization
We study stochastic gradient descent for solving conditional stochastic optimization problems, in which an objective to be minimized is given by a parametric nested expectation with an outer expectation taken with respect to one random variable and an inner conditional expectation with respect to the other random variable.
Unbiased MLMC stochastic gradient-based optimization of Bayesian experimental designs
In this paper, applying the idea of randomized multilevel Monte Carlo (MLMC) methods, we introduce an unbiased Monte Carlo estimator for the gradient of the expected information gain with finite expected squared $\ell_2$-norm and finite expected computational cost per sample.
Toeplitz Monte Carlo
Motivated mainly by applications to partial differential equations with random coefficients, we introduce a new class of Monte Carlo estimators, called Toeplitz Monte Carlo (TMC) estimator for approximating the integral of a multivariate function with respect to the direct product of an identical univariate probability measure.
Numerical Analysis Numerical Analysis Methodology
Efficient Debiased Evidence Estimation by Multilevel Monte Carlo Sampling
In this paper, we propose a new stochastic optimization algorithm for Bayesian inference based on multilevel Monte Carlo (MLMC) methods.
Multilevel Monte Carlo estimation of log marginal likelihood
In this short note we provide an unbiased multilevel Monte Carlo estimator of the log marginal likelihood and discuss its application to variational Bayes.
Decision-making under uncertainty: using MLMC for efficient estimation of EVPPI
In this paper we develop a very efficient approach to the Monte Carlo estimation of the expected value of partial perfect information (EVPPI) that measures the average benefit of knowing the value of a subset of uncertain parameters involved in a decision model.
Numerical Analysis
