In this paper we develop a Maximum Likelihood estimation algorithm for the estimation of infinite mixture distributions. We assume a known conditional distribution, whilst the weighting distribution is assumed unknown and it is approximated by a finite Gaussian mixture. Our approach allows for the correct estimation of the Gaussian mixture parameters. We illustrate the estimation performance of our proposal with numerical simulations.
|Number of pages||6|
|Journal||18th IFAC Symposium on System Identification SYSID 2018: Stockholm, Sweden, 9-11 July 2018|
|State||Published - 1 Jan 2018|
- Gaussian Mixture
- Maximum Likelihood