Jarad Niemi

  1. Efficient Bayesian inference in stochastic chemical kinetic models using graphical processing units.

    Authors: Jarad Niemi, Matthew Wheeler
    Subjects: Computation
    Abstract

    A goal of systems biology is to understand the dynamics of intracellular
    systems. Stochastic chemical kinetic models are often utilized to accurately
    capture the stochastic nature of these systems due to low numbers of molecules.
    Collecting system data allows for estimation of stochastic chemical kinetic
    rate parameters. We describe a well-known, but typically impractical data
    augmentation Markov chain Monte Carlo algorithm for estimating these
    parameters.

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