Dynamic estimation of auditory temporal response functions via state-space models with Gaussian mixture process noise.

ABSTRACT: Estimating the latent dynamics underlying biological processes is a central problem in computational biology. State-space models with Gaussian statistics are widely used for estimation of such latent dynamics and have been successfully utilized in the analysis of biological data. Gaussian statistics, however, fail to capture several key features of the dynamics of biological processes (e.g., brain dynamics) such as abrupt state changes and exogenous processes that affect the states in a structured fashion. Although Gaussian mixture process noise models have been considered as an alternative to capture such effects, data-driven inference of their parameters is not well-established in the literature. The objective of this paper is to develop efficient algorithms for inferring the parameters

SUBMITTER: Miran S 

PROVIDER: S-EPMC7485982 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature