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- W3178628801 abstract "Abstract A common task in experimental sciences is to fit mathematical models to real-world measurements to improve understanding of natural phenomenon (reverse-engineering or inverse modelling). When complex dynamical systems are considered, such as partial differential equations, this task may become challenging or ill-posed. In this work, a linear parabolic equation is considered as a model for protein transcription from MRNA. The objective is to estimate jointly the differential operator coefficients, namely the rates of diffusion and self-regulation, as well as a functional source. The recent Bayesian methodology for infinite dimensional inverse problems is applied, providing a unique posterior distribution on the parameter space continuous in the data. This posterior is then summarized using a Maximum a Posteriori estimator. Finally, the theoretical solution is illustrated using a state-of-the-art MCMC algorithm adapted to this non-Gaussian setting." @default.
- W3178628801 created "2021-07-19" @default.
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- W3178628801 date "2021-07-06" @default.
- W3178628801 modified "2023-09-25" @default.
- W3178628801 title "Bayesian inversion of a diffusion model with application to biology" @default.
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- W3178628801 doi "https://doi.org/10.1007/s00285-021-01621-2" @default.
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