Efficient Bayesian inference for stochastic agent-based models
Fig 4
Performance across different inference schemes for our cancer CA model in terms of the residuals between the mean of the marginal distribution and the true parameter values (θo).
For other metrics, we refer the reader to S9 Fig. The plot includes the results from the emulation-based approaches (emu.) and our direct inference machines (inf.). We include two machine learning approaches: a neural network (NN) and Gaussian processes (GP). In connection with the emulators, we distinguish between results obtained using rejection ABC and MCMC. For each approach, the label specifies the size of the training set: For the emulators, we consistently used 104 simulations.