activity
20162020
collaborators

8 papers

stat.CO2020

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…

math.NA2020

Toeplitz Monte Carlo

Josef Dick, Takashi Goda, Hiroya Murata

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) e…

stat.ML2020

Efficient Debiased Evidence Estimation by Multilevel Monte Carlo Sampling

Kei Ishikawa, Takashi Goda

In this paper, we propose a new stochastic optimization algorithm for Bayesian inference based on multilevel Monte Carlo (MLMC) methods. In Bayesian statistics, biased estimators o…

stat.ML2019

Multilevel Monte Carlo estimation of log marginal likelihood

Takashi Goda, Kei Ishikawa

In this short note we provide an unbiased multilevel Monte Carlo estimator of the log marginal likelihood and discuss its application to variational Bayes.

math.NA2019

Stability of lattice rules and polynomial lattice rules constructed by the component-by-component algorithm

Josef Dick, Takashi Goda

We study quasi-Monte Carlo (QMC) methods for numerical integration of multivariate functions defined over the high-dimensional unit cube. Lattice rules and polynomial lattice rules…

math.NA2019

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…