Finite Gaussian Mixture Approximations to Analytically Intractable Density Kernels

Natalia Khorunzhina, Jean-Francois Richard

Publikation: Working paperForskning


The objective of the paper is that of constructing finite Gaussian mixture approximations to analytically intractable density kernels. The proposed method is adaptive in that terms are added one at the time and the mixture is fully re-optimized at each step using a distance measure that approximates the corresponding importance sampling variance. All functions of interest are evaluated under Gaussian quadrature rules. Examples include a sequential (filtering) evaluation of the likelihood function of a stochastic volatility model where all relevant densities (filtering, predictive and likelihood) are closely approximated by mixtures.
UdgiverMunich Personal RePEc Archive
Antal sider23
StatusUdgivet - 2016
NavnMPRA Paper


  • Finite mixture
  • Distance measure
  • Gaussian quadrature
  • Importance sampling
  • Adaptive algorithm
  • Stochastic volatility
  • Density kernel