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346 14 Bayesian Networks Frequentist (ML) techniques make no a priori assumptions. Bayesian (maximum a priori) techniques start ...

14.3 Constructing Bayesian Networks 347 Classes can inherit from other classes which allows for still more possibil- ities for ...

348 14 Bayesian Networks an entity belongs to the class. Edges are introduced when two classes are related. The most common rela ...

14.3 Constructing Bayesian Networks 349 called “noisy OR-gate” models the combining of evidence in favor of a sin- gle conclusio ...

350 14 Bayesian Networks Figure 14.6 Various informal patterns for BNs. These examples are taken from (Murphy 1998). onciliation ...

14.3 Constructing Bayesian Networks 351 14.3.6 Validating and Revising BNs Testing and validation have always been accepted as a ...

352 14 Bayesian Networks section 14.3.4. The authors have studied the use of their methodology within the domain of esophageal c ...

14.3 Constructing Bayesian Networks 353 When a node is dependent on other nodes, the other nodes (which may otherwise be indepen ...

354 14 Bayesian Networks Figure 14.8 Modifying a BN by reversing the direction of a dependency when two Boolean nodes are relate ...

15 Combining Information Meta-analysisis the integration of data from disparate sources. While this can encompass a wide variety ...

356 15 Combining Information temperature, obtaining 30.2◦±0.3◦C. One now has two independent normal distributions. Combining the ...

15.1 Combining Discrete Information 357 combined random variableZis given by Pr(Z=v)=Pr(X=v|X=Y)= Pr(X=vandX=Y) Pr(X=Y) . Now(X= ...

358 15 Combining Information criteria for making diagnoses. As a result, the diagnoses would not usually be independent. For a m ...

15.2 Combining Continuous Information 359 15.2 Combining Continuous Information One can also combine continuous random variables ...

360 15 Combining Information nand variancesv,w, respectively, then the combined random variable has mean wm+vn v+w = m v+ n w 1 ...

15.3 Information Combination as a BN Design Pattern 361 independent uniform distributions for all of the other nodes. As we note ...

362 15 Combining Information dependency arrows are in the opposite direction. The conditional probabil- ity distributions that d ...

15.4 Measuring Probability 363 Figure 15.3 Examples of information combination processes. The process on the left side combines ...

364 15 Combining Information A normal distribution is characterized by two parameters: the mean and the variance. Consequently, ...

15.5 Dempster-Shafer Theory 365 random sample of size 200. In this case the distribution of the measurement has mean (9.93, 15.5 ...