0.25 0.25 0.25 0.25 -1 0.25 0.25 0.25 0.25 -1 PaymentHistory WorkHistory 0.99 0.01 0.7 0.3 0.7 0.3 0.5 0.5 0.7 0.3 0.55 0.45 0.6 0.4 0.4 0.6 0.196429 0.803571 0.01 0.99 0.1 0.9 0.01 0.99 0.7 0.3 0.3 0.7 0.5 0.5 0.2 0.8 -1 0 11100 25900 50000 0.3333333333333333 0.3333333333333333 0.3333333333333334 1 0 30000 70000 200000 0.3333333333333333 0.3333333333333333 0.3333333333333334 1 Debit Income 0.5 0.5 0.8 0.2 0.999 0.001 0.001 0.999 0.5 0.5 0.8 0.2 0.001 0.999 0.1 0.9 0.5 0.5 -1 0.333333 0.333333 0.333333 -1 Income Assets 0.899 0.1 0.001 0.001 0.3 0.699 0.001 0.1 0.899 0.989 0.01 0.001 0.699 0.3 0.001 0.1 0.8 0.1 0.989 0.01 0.001 0.90734 0.09174300000000001 0.0009170000000000001 0.6899999999999999 0.3 0.01 -1 0.333333 0.333333 0.333333 -1 Worth Profession 0.99 0.01 0.8 0.2 0.6 0.4 0.85 0.15 0.6 0.4 0.4 0.6 0.8 0.2 0.4 0.6 0.01 0.99 -1 16 21 65 100 0.3333333333333333 0.3333333333333333 0.3333333333333334 1 Reliability RatioDebInc FutureIncome Age 0.9 0.1 0.908257 0.09174300000000001 0.8 0.2 0.7 0.3 0.8 0.2 0.6 0.4 0.7 0.3 0.7272729999999999 0.272727 0.7 0.3 0.25 0.75 0.4 0.6 0.25 0.75 0.7 0.3 0.8 0.2 0.5 0.5 0.3 0.7 0.4 0.6 0.2 0.8 0.5 0.5 0.5 0.5 0.4 0.6 0.001 0.999 0.001 0.999 0.001 0.999 -1 A simple network for assessing credit worthiness of an individual. Note that all parentless nodes are described by uniform distributions. This is a weakness of the model, although it is offset by the fact that all these nodes will usually be observed and the network will compute the probability distribution over credit worthiness correctly. Another element of this model is that only the node CreditWorthiness is of interest to the user and is designated as a target.\nReference:\nDeveloped by Gerardina Hernandez as a class homework at the University of Pittsburgh. Payment History 332 39 430 81 Work History 589 39 671 81 Reliability 495 181 573 211 Debit 104 35 174 65 Income 68 139 138 169 Ratio of Debts to Income 282 153 396 206 Assets 39 245 109 275 Worth 236 270 306 300 Profession 131 366 214 396 Future Income 460 369 548 409 Age 648 188 718 218 Credit Worthiness 655 362 750 416 A simple network for assessing credit worthiness of an individual, developed by Gerardina Hernandez as a class homework at the University of Pittsburgh.\nNote that all parentless nodes are described by uniform distributions. This is a weakness of the model, although it is offset by the fact that all these nodes will usually be observed and the network will compute the probability distribution over credit worthiness correctly.\nAnother element of this model is that only the node CreditWorthiness is of interest to the user and is designated as a target.\n 782 28 1135 275