47 citations · 411 across the 42 of their papers we have counts for
5 papers · 1 filter
A simple algorithm for global sensitivity analysis with Shapley effects
Takashi Goda
Global sensitivity analysis aims at measuring the relative importance of different variables or groups of variables for the variability of a quantity of interest. Among several sen…
Unbiased MLMC stochastic gradient-based optimization of Bayesian experimental designs
Takashi Goda, Tomohiko Hironaka, Wataru Kitade +1
In this paper we propose an efficient stochastic optimization algorithm to search for Bayesian experimental designs such that the expected information gain is maximized. The gradie…
Multilevel Monte Carlo estimation of expected information gains
Takashi Goda, Tomohiko Hironaka, Takeru Iwamoto
The expected information gain is an important quality criterion of Bayesian experimental designs, which measures how much the information entropy about uncertain quantity of intere…
Computing the variance of a conditional expectation via non-nested Monte Carlo
Takashi Goda
Computing the variance of a conditional expectation has often been of importance in uncertainty quantification. Sun et al. has introduced an unbiased nested Monte Carlo estimator,…
Unbiased Monte Carlo estimation for the expected value of partial perfect information
Takashi Goda
The expected value of partial perfect information (EVPPI) denotes the value of eliminating uncertainty on a subset of unknown parameters involved in a decision model. The EVPPI can…