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Our paper contributes to recent research on sampling Bayesian network structures from the poste- rior distribution with MCMC. Two principled paradigms have been applied in the past. Structure MCMC, first proposed by Madigan and York, defines a Markov chain in the space of graph struc- tures by ap ...

There have been various attempts to improve the reconstruction of gene regulatory networks from microarray data by the systematic integration of biological prior knowledge. Our approach follows the Bayesian paradigm where the prior knowledge is expressed in terms of energy functions, from which ...

Parameter inference in mechanistic models based on non-affine differential equations is computationally onerous, and various faster alternatives based on gradient matching have been proposed. A particularly promising approach is based on nonparametric Bayesian modelling with Gaussian processes, whic ...