### Table 4.7: Table of electron candidates and estimated background in bins of p and pt.

### Table 1 Estimated Phillips curves (MG 1968:1-1979:4, FE 1960:1-1979:4) pt pt pt pt

### Table 5.4: The lexical entailment probability estimation process - P0 represents the initial labeling and P1 the Bayesian estimation for P(T rjob = 1|t)

2006

### Table 3: Results of simulations with random sequences and uniformly distributed clone positions. Pt estimates the fraction of times the reconstructed sequence di ers from the target sequence for false-negative probability t. Et estimates the fraction of incorrectly reconstructed positions in the sequence.

2002

"... In PAGE 20: ... We also computed Et, the average over all runs of the fraction of positions in which At and A di er, for t = 0; 0:5.) Table3 contains the results. While the odds of completely correct reconstruction decrease with target size, the average number of mismatch errors in the reconstruction was very low: between 2 and 4 in 10,000 bp.... ..."

Cited by 14

### Table 3: Results of simulations with random sequences and uniformly distributed clone positions. Pt estimates the fraction of times the reconstructed sequence differs from the target sequence for false-negative probability t. Et estimates the fraction of incorrectly reconstructed positions in the sequence.

2002

"... In PAGE 20: ...5.) Table3 contains the results. While the odds of completely correct reconstruction decrease with target size, the average number of mismatch errors in the reconstruction was very low: between 2 and 4 in 10,000 bp.... ..."

Cited by 14

### Table 3: Results of simulations with random sequences and uniformly distributed clone positions. Pt estimates the fraction of times the reconstructed sequence differs from the target sequence for false-negative probability t. Et estimates the fraction of incorrectly reconstructed positions in the sequence.

2002

"... In PAGE 20: ...5.) Table3 contains the results. While the odds of completely correct reconstruction decrease with target size, the average number of mismatch errors in the reconstruction was very low: between 2 and 4 in 10,000 bp.... ..."

Cited by 14

### Table 5: Computational Results for the 9-Task Example Model Characteristics 7-pt Model 21-pt Model

2003

"... In PAGE 28: ... Figure 14 displays the technological precedences for this problem, while Figure 15 displays the optimal schedule when the computations are performed with 20 intervals (21 points). Computa- tional results from two discretizations are shown in Table5 . Both models provide the same optimal solution, although the 21-pt discretization model provides a better estimate of the mean value of the profit.... ..."

Cited by 3

### Table 4: Aggregate Price Adjustmenta pt = ?a(1)[pt?1 ? p t?1] + a (L) pt?1

"... In PAGE 23: ...Table4 presents estimates of the pricing equation under alternative assumptions about the degree of the factor polynomial, m. As noted in the table, the sample span is 1963Q1 - 1999Q4, and p-values are reported in parentheses.... In PAGE 23: ... Second, the rational expectations overidentifying restrictions, consisting of nonlinear restrictions on the coefficients of the VAR forecast model and the parameters of the factor polynomial, are also strongly rejected, with a zero p-value. The column headed by m = 2 in Table4 lists results of the estimated pricing equation for a second-order factor polynomial. (There was no empirical support for m gt; 2.... ..."

Cited by 1

### Table 3: The accuracy of the systems that estimate probabilities based on only one feature vector containing all the extracted features (1) Extracted feature: pt1, pt2, hw1, hw2, voice1, voice2, depPath, synCrossPath (2) Extracted feature: pt1, pt2, hw1, hw2, voice1, voice2, depPath, normalPath

"... In PAGE 4: ... Therefore, the training data is sparse to do statistics. Table3 shows the poor result of such system. To estimate the probability for such the cases, we reduce the restriction of the condition in the probability formula by dividing the general feature vector into smaller vectors.... ..."