Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data
Fig 9
Sampling efficiency of filter inference variants.
The figure shows the minimum ESS across dimensions as a function of log-posterior evaluations for different posterior distributions inferred with: 1. NUTS and the deterministic filter posterior (blue); and 2. MH and the stochastic filter posterior (orange). For NUTS, the number of evaluations include evaluations of the log-posterior gradient. For MH, the log-posterior gradient is not evaluated. Panels 1, 2, 3 and 4 show the minimum ESS of the cancer growth model posteriors from Fig 4 and panel 5 shows the EGF pathway model posteriors from Fig 6B.