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Table 9 Expected errors of different Bayesian classification rules in the mixture model for the TP53 network. Expected true error (left) and expected error on unlabeled training data (right), with c 0=0.6

From: Incorporating biological prior knowledge for Bayesian learning via maximal knowledge-driven information priors

Method/ n

15

30

45

60

75

Method/ n

15

30

45

60

75

PDCOTP

0.2746

0.2824

0.2829

0.2996

0.2960

PDCOTP

0.2762

0.2818

0.2900

0.3027

0.2900

Jeffreys’

0.4204

0.4324

0.4335

0.4432

0.4361

Jeffreys’

0.4220

0.4314

0.4381

0.4419

0.4348

RMEP

0.3274

0.3204

0.3327

0.3402

0.3422

RMEP

0.3471

0.3350

0.3487

0.3543

0.3529

RMDIP

0.3297

0.3260

0.3327

0.3406

0.3432

RMDIP

0.3504

0.3423

0.3496

0.3551

0.3545

REMLP

0.3637

0.3687

0.3706

0.3658

0.3653

REMLP

0.3489

0.3579

0.3709

0.3593

0.3556

MKDIP-E

0.3312

0.3246

0.3322

0.3428

0.3386

MKDIP-E

0.3502

0.3378

0.3486

0.3585

0.3492

MKDIP-D

0.3321

0.3204

0.3306

0.3436

0.3366

MKDIP-D

0.3551

0.3329

0.3473

0.3570

0.3475

MKDIP-R

0.3872

0.3749

0.3667

0.3607

0.3586

MKDIP-R

0.3613

0.3583

0.3589

0.3539

0.3462

  1. The lowest error for each sample size and the lowest error among practical methods is written in bold