A hierarchical Bayesian inference model for volatile multivariate exponentially distributed signals.

ABSTRACT: Brain activities often follow an exponential family of distributions. The exponential distribution is the maximum entropy distribution of continuous random variables in the presence of a mean. The memoryless and peakless properties of an exponential distribution impose difficulties for data analysis methods. To estimate the rate parameter of multivariate exponential distribution from a time series of sensory inputs (i.e., observations), we constructed a hierarchical Bayesian inference model based on a variant of general hierarchical Brownian filter (GHBF). To account for the complex interactions among multivariate exponential random variables, the model estimates the second-order interaction of the rate intensity parameter in logarithmic space. Using variational Bayesian scheme, a family o

SUBMITTER: Zhu C 

PROVIDER: S-EPMC12648510 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature