Automated Bayesian model development for frequency detection in biological time series.

ABSTRACT:

Background

A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies.

Results

In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages

SUBMITTER: Granqvist E 

PROVIDER: S-EPMC3149002 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature