Name
Abstract | ||
|---|---|---|
Bayesian quadrature (BQ) is a method for solving numerical integration problems in a Bayesian manner, which allows user to quantify their uncertainty about the solution. The standard approach to BQ is based on Gaussian process (GP) approximation of the integrand. As a result, BQ approach is inherently limited to cases where GP approximations can be done in an efficient manner, thus often prohibiting high-dimensional or non-smooth target functions. This paper proposes to tackle this issue with a new Bayesian numerical integration algorithm based on Bayesian Additive Regression Trees (BART) priors, which we call BART-Int. BART priors are easy to tune and well-suited for discontinuous functions. We demonstrate that they also lend themselves naturally to a sequential design setting and that explicit convergence rates can be obtained in a variety of settings. The advantages and disadvantages of this new methodology are highlighted on a set of benchmark tests including the Genz functions, and on a Bayesian survey design problem. |
Year | Venue | DocType |
|---|---|---|
2020 | NIPS 2020 | Conference |
Volume | Citations | PageRank |
33 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhu Harrison | 1 | 0 | 0.34 |
| Liu Xing | 2 | 0 | 0.34 |
| Kang Ruya | 3 | 0 | 0.34 |
| Shen Zhichao | 4 | 0 | 0.34 |
| Seth Flaxman | 5 | 122 | 9.00 |
| François-Xavier Briol | 6 | 19 | 3.96 |