33 citations · 33 across the 3 of their papers we have counts for
3 papers
Bayesian spline-based hidden Markov models with applications to actimetry data and sleep analysis
Sida Chen, Bärbel Finkenstädt Rand
B-spline-based hidden Markov models employ B-splines to specify the emission distributions, offering a more flexible modelling approach to data than conventional parametric HMMs. W…
Quantifying intrinsic and extrinsic noise in gene transcription using the linear noise approximation: An application to single cell data
Bärbel Finkenstädt, Dan J. Woodcock, Michal Komorowski +4
A central challenge in computational modeling of dynamic biological systems is parameter inference from experimental time course measurements. However, one would not only like to i…
Bayesian inference of biochemical kinetic parameters using the linear noise approximation
Michal Komorowski, Barbel Finkenstadt, Claire V. Harper +1
Fluorescent and luminescent gene reporters allow us to dynamically quantify changes in molecular species concentration over time on the single cell level. The mathematical modeling…