24 citations · 48 across the 5 of their papers we have counts for
5 papers
Estimating nested expectations without inner conditional sampling and application to value of information analysis
Tomohiko Hironaka, Takashi Goda
Motivated by various computational applications, we investigate the problem of estimating nested expectations. Building upon recent work by the authors, we propose a novel Monte Ca…
An efficient estimation of nested expectations without conditional sampling
Tomohiko Hironaka, Takashi Goda
Estimating nested expectations is an important task in computational mathematics and statistics. In this paper we propose a new Monte Carlo method using post-stratification to esti…
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 the expected value of sample information
Tomohiko Hironaka, Michael B. Giles, Takashi Goda +1
We study Monte Carlo estimation of the expected value of sample information (EVSI) which measures the expected benefit of gaining additional information for decision making under u…
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…