### lable but not directly influence the scheduling decisions.

Cited by 2

### Table V. LOD Scores and Direction of Effect of QTLs Influencing Autonomic Behavior

2004

Cited by 1

### Table 3: Domain knowledge for ambiguity-directed sampling in influence-based model decomposition.

2001

Cited by 9

### Table 3: Domain knowledge for ambiguity-directed sampling in influence-based model decomposition.

2001

Cited by 9

### Table 1 Relative costs of SIMD operations. For a selection of ISA extensions, the actual number of SIMD instructions required to implement the respective SIMD operations is given. This data directly influences the rule ranking underlying the MAP vectorizer.

### Table II. LOD Scores and Direction of Effect of QTLs Influencing General Locomotor Activity

2004

Cited by 1

### Table 2: Influence of the characteristics of the input image on the characteristics of the output image. For instance, the size of an input object influences the size, the activity and the period of the response. Gap stands for the distance between two input objects. Direction is the direction of the movement. It influences the orientation of the response which is perpendicular to the movement direction.

1998

Cited by 1

### Table 4: The three unconfounded causal hypotheses being modeled. The double-headed arcs convey that the causal influence is either direct (relative to the modeled variables) or indirect.

1999

"... In PAGE 6: ... We did so to simplify the experimental design and analysis in this initial experiment. Table4 shows the three possible modeled relationships that can exist between two nodes that are not confounded: (1) there is one or more causal paths from X 6 In particular, we used the version of ALARM that is publicly available for downloading as alarm.dsc from the Bayesian Network Repository at http://www-nt.... In PAGE 7: ... For each of the 81 pairs of unconfounded nodes, we used the method in Section 2.1 to compute a posterior probability distribution over the three causal network structures in Table4 . We assumed a uniform prior probability of 1/3 for each structure.... In PAGE 7: ... 3.3 EVALUATION METRICS For a given node pair (X, Y), let Htrue designate which of the three structures from Table4 is the relationship between X and Y in ALARM. For each pair (X, Y) and dataset D, we derived the following structural error metric: SErrX,Y(D) = 1 - P(Htrue | D, K), where P(Htrue | D, K) is the posterior probability derived by using the method in Section 2.... ..."

Cited by 43

### Table 4: The three unconfounded causal hypotheses being modeled. The double-headed arcs convey that the causal influence is either direct (relative to the modeled variables) or indirect.

1999

"... In PAGE 6: ... We did so to simplify the experimental design and analysis in this initial experiment. Table4 shows the three possible modeled relationships that can exist between two nodes that are not confounded: (1) there is one or more causal paths from X 6 In particular, we used the version of ALARM that is publicly available for downloading as alarm.dsc from the Bayesian Network Repository at http://www-nt.... In PAGE 7: ... For each of the 81 pairs of unconfounded nodes, we used the method in Section 2.1 to compute a posterior probability distribution over the three causal network structures in Table4 . We assumed a uniform prior probability of 1/3 for each structure.... In PAGE 7: ... 3.3 EVALUATION METRICS For a given node pair (X, Y), let Htrue designate which of the three structures from Table4 is the relationship between X and Y in ALARM. For each pair (X, Y) and dataset D, we derived the following structural error metric: SErrX,Y(D) = 1 - P(Htrue | D, K), where P(Htrue | D, K) is the posterior probability derived by using the method in Section 2.... ..."

Cited by 43

### Table 4: The three unconfounded causal hypotheses being modeled. The double-headed arcs convey that the causal influence is either direct (relative to the modeled variables) or indirect.

1999

"... In PAGE 6: ... We did so to simplify the experimental design and analysis in this initial experiment. Table4 shows the three possible modeled relationships that can exist between two nodes that are not confounded: (1) there is one or more causal paths from X 6 In particular, we used the version of ALARM that is publicly available for downloading as alarm.dsc from the Bayesian Network Repository at http://www-nt.... In PAGE 7: ... For each of the 81 pairs of unconfounded nodes, we used the method in Section 2.1 to compute a posterior probability distribution over the three causal network structures in Table4 . We assumed a uniform prior probability of 1/3 for each structure.... In PAGE 7: ... 3.3 EVALUATION METRICS For a given node pair (X, Y), let Htrue designate which of the three structures from Table4 is the relationship between X and Y in ALARM. For each pair (X, Y) and dataset D, we derived the following structural error metric: SErrX,Y(D) = 1 - P(Htrue | D, K), where P(Htrue | D, K) is the posterior probability derived by using the method in Section 2.... ..."

Cited by 43