Non-parametric Sampling Approximation via Voronoi Tessellations

作者：Villagran Alejandro; Huerta Gabriel^*; Vannucci Marina; Jackson Charles S; Nosedal Alvaro

来源：Communications in Statistics - Simulation and Computation, 2016, 45(2): 717-736.

DOI：10.1080/03610918.2013.870798

• Climate Models
• Non-parametric approximation
• Parameter estimation
• Voronoi tessellation
• 62
• 62F15
• 62G09
• 62P12
• 86-08
• 65C40

In this article we propose a novel non-parametric sampling approach to estimate posterior distributions from parameters of interest. Starting from an initial sample over the parameter space, this method makes use of this initial information to form a geometrical structure known as Voronoi tessellation over the whole parameter space. This rough approximation to the posterior distribution provides a way to generate new points from the posterior distribution without any additional costly model evaluations. By using a traditional Markov Chain Monte Carlo (MCMC) over the non-parametric tessellation, the initial approximate distribution is refined sequentially. We applied this method to a couple of climate models to show that this hybrid scheme successfully approximates the posterior distribution of the model parameters.

• 出版日期2016