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)

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