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Table 5 (Ex-Sect-9.1). For each algorithm, the table shows the mean square error (MSE), the autocorrelation (ρ(τ)) at different lags, the effective sample size (ESS), the final number of support points (m T ), the computing times normalized w.r.t. ARMS (Time)

From: Adaptive independent sticky MCMC algorithms

Algorithm MSE ρ(1) ρ(10) ρ(50) ESS m T Time
ARMS [12] 10.04 0.4076 0.3250 0.2328 89.12 118.19 1.00
AISM-P1-R3 3.0277 0.1284 0.1099 0.0934 235.76 152.63 1.23
AISM-P2-R3 2.9952 0.1306 0.1125 0.0929 235.01 71.14 0.27
AISM-P3-R3 0.0290 0.0535 0.0165 0.0077 609.05 279.65 0.65
AISM-P4-R3 0.0354 0.0354 0.0195 0.0086 608.76 84.87 0.33
AISMTM-P1 (M=10) 0.6720 0.0726 0.0696 0.0624 336.84 159.01 2.35
R3 (M=50) 0.1666 0.0430 0.0395 0.0316 617.10 160.75 5.45
AISMTM-P2 (M=10) 0.5632 0.0588 0.0525 0.0443 440.23 72.16 1.13
R3 (M=50) 0.1156 0.0345 0.0303 0.0231 746.45 72.53 4.38
AISMTM-P3 (M=10) 0.0105 0.0045 0.0001 0.0001 4468.10 315.78 2.60
R3 (M=50) 0.0099 0.0041 0.0001 0.0001 4843.81 360.73 10.59
AISMTM-P4 (M=10) 0.0108 0.0036 0.0011 0.0014 3678.79 92.67 1.86
R3 (M=50) 0.0098 0.0001 0.0001 0.0001 4912.07 101.78 7.25
AISM-P4-R2 (ε=0.01) 0.0412 0.0407 0.0213 0.0074 604.95 35.01 0.11
(ε=0.005) 0.0321 0.0360 0.0181 0.0072 610.01 43.32 0.20
AISM-P4-R1 (β=0.3) 0.1663 0.2710 0.1368 0.0593 216.75 25.56 0.08
(β=0.7) 0.1046 0.1781 0.0866 0.0441 356.21 33.55 0.11
(β=2) 0.0824 0.0947 0.0408 0.0204 677.73 46.81 0.21
(β=3) 0.0371 0.0720 0.0281 0.0099 714.90 52.76 0.23
(β=4) 0.0310 0.0621 0.0253 0.0096 802.18 58.66 0.24