### Table 1 Timing for the Monte Carlo algorithm

"... In PAGE 7: ... Our approach works for some non-diagonally dominant matrices [4] as well. The results of the experiments are shown in Table1 , and Fig. 4 shows the speedup achieved by the algorithm.... ..."

### Table 1. Prediction errors and run-time for Monte-Carlo and for bounding algorithm (double entries are values for 20/50 paths).

"... In PAGE 5: ... The bounds generated by the algorithm follow the Monte Carlo distribution very closely (Figure 1 and 2a). Table1 shows the errors of both upper and lower bounds with respect to the exact distribution. The bounds were computed for N=20 and 50 longest paths.... In PAGE 5: ...enchmark circuits is 0.7%. Figure 2b demonstrates that accurately accounting for node delay correlations is crucial in predicting the shape of cdf: the mean value of an uncorrelated case is larger than that of the correlated one, while the spread is much smaller. Table1 also contains the evaluation of run time of the algorithm. The Monte Carlo (for 1000 samples) is substantially slower than our algorithm.... ..."

### Table 3. Run-time results for the Monte Carlo algorithm Notice that in some cases the relative slow-downs are slightly below 1, i.e. the Skil run-times even beat the C run-times. The reason is that the C implementa- tion referred to here is an older version, which does not use virtual topologies or asynchronous communication, as our skeleton implementation does. Of course, a Skil program could never beat an equally well optimized C version of that program, since Skil is translated to message-passing C.

1996

"... In PAGE 11: ... The procedure is given below. double monte_carlo (array lt;double gt; a, double x0, double y0, double h, int n) { double res ; a = array_create (1, {n,0}, {0,0}, {-1,-1}, rand_traj (x0, y0, h, n/netSize), DISTR_DEFAULT) ; res = array_fold (ident, (+), a) / n ; array_destroy (a) ; return (res) ; } The run-times obtained for the Skil implementation of this algorithm are given in Table3 . We have compared these results with those obtained for the Parix-C and DPFL programs, respectively.... ..."

Cited by 13

### Table 1. Comparison results of our algorithm with Monte Carlo Simulation Benchmark Error compared with MC Run Time (s) Name #Cells #Grids Avg Error (%) Max Error (%) Our MC (30000)

2005

"... In PAGE 3: ... We chose to run 30,000 iterations for Monte Carlo simulation. The average and maximum errors of timing yield are shown in Table1 . We can see that our algorithm can get very accurate timing yield results.... ..."

Cited by 1

### Table 1: Our Results All our results are based on the reduction of geometric matching to combinatorial pattern matching; in each case the output is a bit vector o of translations such that o[t] = 1 i t(P) and Q have a match with the desired properties. All running times that we report are for algorithms that output such a bit vector. The algorithms are Monte Carlo; the probability of error in each case is O(1=nc); c 1.

2000

Cited by 3

### Table 4: Prices computed by alternative methods under the 3-factor SV model

2000

"... In PAGE 15: ... For both methods however, increasing the number of strikes does not result in dramatic increases in the com- putational times. Table4 shows the spread option prices for di erent strikes under the three factor SV model. The Monte Carlo prices with a discretisation of 2000 time steps oscillate around those computed by the FFT method.... ..."

Cited by 5

### Table 4 Monte Carlo Evidence

2005

"... In PAGE 14: ... The results of the six experiments are presented in Table 4. Table 4 Panel A of Table4 gives the actual quarterly IMRF where the underlying model is difference stationary and three FRFs derived from three alternative estimation strategies. For all three estimation strategies the resulting FRFs give large values of persistence that grow with the time horizon.... In PAGE 15: ... What do we make of these results? First, we may ask what these results imply about the estimation strategy employed by forecasters. From Table4 we see that the strategy of estimating low order ARMA models, omitting a time trend and leaving the largest root unconstrained yield very large and increasing FRFs. Because these estimates are much higher than any observed from the actual forecast revisions in Table 3, we may conclude that forecasters have not used this strategy.... In PAGE 15: ... Because these estimates are much higher than any observed from the actual forecast revisions in Table 3, we may conclude that forecasters have not used this strategy. Table4 indicates that the strategy of imposing a time trend yields moderate estimates of persistence when the underlying process is a unit root but very low estimates when the true process is trend stationary. The latter case is inconsistent with observed FRFs from Table 3.... In PAGE 15: ... Finally, the strategy of imposing a unit root on low order AR models yields FRFs somewhat higher than those actually observed in Table 3. More generally, the results in Table4 suggest that annual FRFs will tend to be substantially larger than underlying quarterly IMRFs. In that sense, annual FRFs provide very poor and upwardly biased estimates of the underlying quarterly IMRF.... In PAGE 15: ... If our interest is obtaining the underlying IMRF from quarterly data, we may conclude that the large estimates of persistence found in the previous section are merely artifacts of the estimation process and have little bearing on the question of whether shocks to output are persistent. An alternative interpretation of the results in Table4 is warranted. The FRFs represent how forecasters actually revise their forecasts in light of new information and ... In PAGE 16: ... In practice, forecasters, and presumably economic agents, do not have precise knowledge of the underlying model and will use new information to update their model specification. In that sense, the results of Table4 can be interpreted as implying that IMRFs provide a poor estimate of how shocks affect our forecasts of future levels of output and hence are poor measures of the persistence of shocks. ... ..."

### TABLE 1.1 Examples of Commonly Used Structure-Based Drug Design Packages

### Table 2 Results of the Monte Carlo study

"... In PAGE 5: ... It identifies the true model 83.2% of the times ( Table2 ). The main problem associated with AIC is that it tends to overfit the data (20.... ..."

### Table 17. Monte Carlo results for detection performance

"... In PAGE 7: ...able 16. Power management for Power PC 750..........................................................................................30 Table17 .... In PAGE 38: ... Of course since the true nature of each signal is known, detection performance numbers can be determined for each algorithm. The results of the experiment are shown in Table17 . The experiment result for the probability of detection of the analog trigger box was 54.... ..."