Table 3.

Error of fit between CDR data and synthetic data

CDR	$\mathit{\Delta }r$	$r_g$	$S^{\mathit{unc}}$	T	D	$\mathit{\Delta }t$	V	N	f(L)
MD
$\mathit{d}$-EPR	.0001	.0026	.9643	.0061	.0659	.0014	$2.6E^{-5}$	.0218	.0122
	.0006	.0247	29.34	.0101	.0682	.1915	.0016	.5449	.1200
SWIM	.0005	–	3.6069	.0062	.0683	.0029	$5.6E^{-5}$	–	.0669
	.0067		60.97	.0101	.0808	.4996	.0451		1.2892
LATP	.0001	.0061	3.2236	.0062	.0684	.0027	$6.3E^{-5}$	–	.0625
	.0008	.3223	258.46	.0101	.0802	.3282	.0600		.9353
RD
d-EPR	.0004	.0027	1.1745	.0232	.2098	.0024	$4.1E^{-5}$	.0235	.0521
	.0029	.0161	20.8015	.197	4.3558	.2048	.0191	1.1773	.3876
SWIM	.0041	–	–	.0232	–	.0033	$7.2E^{-5}$	–	.0947
	.1501			.1974		.3773	.0460		4.4057
LATP	.0002	–	–	.0232	–	.0033	$4.6E^{-5}$	–	.0874
	.0014			.1974		.6967	.0321		2.2051
WT
d-EPR	.0003	.0024	1.1666	.0232	.1790	.0023	$4.0E^{-5}$	.0224	.0502
	.0019	.0130	20.00	.1970	3.9769	.1946	.0189	1.0395	.3537
SWIM	.0033	–	–	.0232	.2036	.0033	$1.9E^{-5}$	–	.0943
	.0601			.1975	4.3806	.1146	.0070		3.9605
LATP	.0001	–	–	.0232	.2037	.0033	$7.2E^{-5}$	–	.0866
	.0010			.1975	4.5672	.6322	.0309		2.1015
Best model	d-EPR	d-EPR	d-EPR	d-EPR	d-EPR	d-EPR	SWIM	d-EPR	d-EPR
	MD	WT	MD	MD	MD	MD	WT	MD	MD

Every row i is a model and every column j a mobility measure. A cell (i, j) indicates the RMSE (first row) and the KL divergence (second row) of a synthetic distribution w.r.t. the real distribution. The best RMSE values are in italic. Symbol—indicates that the synthetic distribution is not comparable with the real distribution. We highlight in bold the combination of temporal and spatial model leading to the highest number of Italic cells