1 code implementation • 9 May 2019 • Wilson Ye Chen, Alessandro Barp, François-Xavier Briol, Jackson Gorham, Mark Girolami, Lester Mackey, Chris. J. Oates
Stein Points are a class of algorithms for this task, which proceed by sequentially minimising a Stein discrepancy between the empirical measure and the target and, hence, require the solution of a non-convex optimisation problem to obtain each new point.
1 code implementation • ICML 2018 • Wilson Ye Chen, Lester Mackey, Jackson Gorham, François-Xavier Briol, Chris. J. Oates
An important task in computational statistics and machine learning is to approximate a posterior distribution $p(x)$ with an empirical measure supported on a set of representative points $\{x_i\}_{i=1}^n$.
1 code implementation • 25 Aug 2017 • Wilson Ye Chen, Richard H. Gerlach
As the dynamic structure of the financial markets is subject to dramatic changes, a model capable of providing consistently accurate volatility estimates must not make strong assumptions on how prices change over time.
Methodology Risk Management Applications
no code implementations • ICML 2017 • Francois-Xavier Briol, Chris. J. Oates, Jon Cockayne, Wilson Ye Chen, Mark Girolami
The standard Kernel Quadrature method for numerical integration with random point sets (also called Bayesian Monte Carlo) is known to converge in root mean square error at a rate determined by the ratio $s/d$, where $s$ and $d$ encode the smoothness and dimension of the integrand.