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Table 4 Median delays F-scores of case I using multiple short time series with D-CLINDE and GlobalMIT*

From: High-order dynamic Bayesian Network learning with hidden common causes for causal gene regulatory network

   

p bias =0.65

p bias =0.75

p bias =0.85

p

c

K

D-CLINDE

GlobalMIT*

D-CLINDE

GlobalMIT*

D-CLINDE

GlobalMIT*

0

2

4

0.000

0.000

0.000

0.250

0.000

0.000

  

8

0.000

0.000

0.833

0.833

0.000

0.000

  

16

0.250

0.833

1.000

1.000

0.250

0.250

  

32

1.000

1.000

0.900

1.000

0.650

0.650

 

3

4

0.450

0.400

0.667

0.733

0.800

0.800

  

8

0.733

0.800

0.667

0.733

0.667

0.800

  

16

0.667

0.800

0.800

0.800

0.667

0.667

  

32

0.829

0.800

0.829

0.800

0.667

0.800

 

4

4

0.536

0.619

0.619

0.762

0.762

0.857

  

8

0.667

0.750

0.750

0.857

0.750

0.750

  

16

0.750

0.857

0.750

0.804

0.708

0.857

  

32

0.857

0.929

0.857

0.857

0.750

0.804

 

5

4

0.558

0.586

0.495

0.586

0.667

0.750

  

8

0.550

0.619

0.697

0.800

0.667

0.750

  

16

0.800

0.764

0.889

0.899

0.633

0.739

  

32

0.889

0.889

0.889

0.889

0.697

0.750

1

2

4

0.400

0.400

0.486

0.533

0.619

0.800

  

8

0.452

0.533

0.667

0.800

0.619

0.733

  

16

0.667

0.800

0.667

0.733

0.733

0.800

  

32

0.775

0.800

0.667

0.733

0.667

0.800

 

3

4

0.310

0.367

0.571

0.571

0.536

0.667

  

8

0.571

0.667

0.804

0.857

0.750

0.857

  

16

0.857

0.857

0.873

0.873

0.804

0.857

  

32

0.857

0.857

0.829

0.873

0.829

0.873

 

4

4

0.472

0.500

0.500

0.571

0.800

0.889

  

8

0.500

0.536

0.667

0.775

0.899

1.000

  

16

0.697

0.800

0.739

0.775

0.861

1.000

  

32

0.817

0.899

0.800

0.800

0.899

0.955

 

5

4

0.545

0.727

0.667

0.823

0.550

0.573

  

8

0.641

0.667

0.718

0.909

0.780

0.855

  

16

0.861

0.909

0.845

0.909

0.833

0.909

  

32

0.899

0.909

0.857

0.916

0.769

0.883

2

2

4

0.286

0.333

0.500

0.571

0.310

0.367

  

8

0.400

0.400

0.633

0.750

0.125

0.200

  

16

0.571

0.667

0.750

0.873

0.250

0.333

  

32

0.586

0.667

0.829

0.944

0.571

0.667

 

3

4

0.286

0.393

0.495

0.571

0.472

0.536

  

8

0.389

0.417

0.697

0.889

0.573

0.750

  

16

0.500

0.571

0.800

0.800

0.800

0.889

  

32

0.708

0.750

0.775

0.775

0.764

0.861

 

4

4

0.382

0.444

0.545

0.633

0.600

0.523

  

8

0.500

0.764

0.727

0.817

0.580

0.697

  

16

0.748

0.785

0.833

0.909

0.801

0.871

  

32

0.833

0.813

0.909

0.962

0.833

0.909

 

5

4

0.333

0.431

0.571

0.718

0.678

0.688

  

8

0.545

0.608

0.775

0.845

0.769

0.812

  

16

0.667

0.748

0.800

0.923

0.828

0.801

  

32

0.933

0.923

0.857

0.923

0.857

0.923

3

2

4

0.222

0.250

0.400

0.389

0.250

0.365

  

8

0.000

0.000

0.422

0.468

0.472

0.675

  

16

0.310

0.268

0.633

0.750

0.667

0.819

  

32

0.472

0.500

0.727

0.800

0.667

0.750

 

3

4

0.222

0.343

0.400

0.500

0.600

0.600

  

8

0.422

0.500

0.472

0.600

0.606

0.697

  

16

0.500

0.550

0.641

0.764

0.748

0.855

  

32

0.573

0.667

0.769

0.817

0.785

0.909

 

4

4

0.308

0.414

0.445

0.464

0.714

0.727

  

8

0.429

0.511

0.690

0.727

0.813

0.801

  

16

0.523

0.586

0.760

0.923

0.785

0.890

  

32

0.690

0.739

0.800

0.923

0.857

0.899

 

5

4

0.354

0.388

0.517

0.667

0.667

0.667

  

8

0.517

0.615

0.607

0.746

0.789

0.857

  

16

0.533

0.769

0.787

0.857

0.881

0.933

  

32

0.778

0.857

0.775

0.866

0.833

0.933