STILT - Stochastic Inference on Lineage Trees

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STILT is a software package for performing exact Bayesian parameter inference and model selection on the basis of quantified time-lapse fluorescence microscopy movies of populations of proliferating cells. STILT uses a particle filter-based approach to infer the latent history of unobserved biochemical species and the posterior distribution of biochemical parameters, for user-specified mechanistic, stochastic chemical reaction/gene regulation models. It estimates the marginal likelihood (evidence) of the specified models, from which one can rank candidate models and perform model selection using Bayes Factors. Using a goodness-of-fit test, candidate models and inferred model parameters can be compared for plausibility in the context of measured data.

The STILT software (including example data) is available on Github at:

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Mon Jun 26 12:35:03 CEST 2017
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