Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data
Fig 8
Computational costs of filter inference and traditional NLME inference II—Number of log-posterior evaluations.
The figure shows the number of log-posterior evaluations of traditional NLME inference (blue lines) and filter inference, with a Gaussian filter and S = 100 simulated individuals, (red lines) for the early cancer growth model and the EGF pathway model using varying sizes of snapshot datasets. Each log-posterior evaluation includes the evaluation of its gradient. The posterior distributions are inferred using 1000 MCMC iterations of NUTS after calibrating the algorithm for 500 iterations.