Convolutional Neural Networks as Summary Statistics for Approximate Bayesian Computation

Understand

Approximate Bayesian Computation is widely used in systems biology for inferring parameters in stochastic gene regulatory network models.

• Its performance hinges critically on the ability to summarize high-dimensional system responses such as time series into a few informative, low-dimensional summary statistics.
• The quality of those statistics acutely impacts the accuracy of the inference task.
• Existing methods to select the best subset out of a pool of candidate statistics do not scale well with large pools of several tens to hundreds of candidate statistics.