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Table 1: Posterior density summary of ideal points of U.S. Supreme Court Justices, 1953-1999, for the constant ideal point model.
Table 1: Posterior density summary of ideal points of U.S. Supreme Court Justices, 1953-1999, for the constant ideal point model.
2001
Table 1: Posterior density summary of ideal points of U.S. Supreme Court Justices, 1953-1999, for the constant ideal point model. BCI denotes the Bayesian credible interval.
in Dynamic Ideal Point Estimation via Markov Chain Monte Carlo for the U.S. Supreme Court, 1953-1999
2002
Cited by 13
Table 4: Posterior density summary for one-dimensional item response models with an ordinal prior on the ideal points. BCI denotes the Bayesian Credible Interval.
2001
Cited by 1
Table 4: Distribution of the increments on the II and on the density of memory tra c relative to the ideal con gurations (i.e. without spill code) hand if we assume a limited number of registers (e.g. 32) the speedup produced by doubling the number of functional units depends on the latency of functional units, 1.43 for latency 3 and 1.2 for latency 6, due to the e ects of spill code. Comparing the gures with those obtained by the Livermore Loops, one can see that real applications bene t more from doubling the number functional units, but are also more a ected by spill code.
1994
"... In PAGE 12: ....43 for latency 3 and 1.2 for latency 6, due to the e ects of spill code. Comparing the gures with those obtained by the Livermore Loops, one can see that real applications bene t more from doubling the number functional units, but are also more a ected by spill code. Table4 shows a distribution of loops based on the number of cycles their initiation interval has increased when a limited number of registers is considered. For example, for con guration P1L3 with 32 registers 2.... ..."
Cited by 15
Table 4: Distribution of the increments on the II and on the density of memory tra c relative to the ideal con gurations (i.e. without spill code) hand if we assume a limited number of registers (e.g. 32) the speedup produced by doubling the number of functional units depends on the latency of functional units, 1.43 for latency 3 and 1.2 for latency 6, due to the e ects of spill code. Comparing the gures with those obtained by the Livermore Loops, one can see that real applications bene t more from doubling the number functional units, but are also more a ected by spill code.
"... In PAGE 12: ....43 for latency 3 and 1.2 for latency 6, due to the e ects of spill code. Comparing the gures with those obtained by the Livermore Loops, one can see that real applications bene t more from doubling the number functional units, but are also more a ected by spill code. Table4 shows a distribution of loops based on the number of cycles their initiation interval has increased when a limited number of registers is considered. For example, for con guration P1L3 with 32 registers 2.... ..."
Table A.4: P[S lt; t] for M/M/1 queue with = 0:8, = 1 Any interval that covers a 1 ? of the area under the density function can be considered a con dence interval. Ideally, the tightest such interval is desired. For ease of computation, however, it is recommended [98, p. 208] to pick equal tails. For example, given a 90% con dence interval, the lower bound is arg(P[S lt; t]) = 0:05 and the upper bound is arg(P[S lt; t]) = 0:95. Note that the bound will in general not be symmetrical around the point estimate.
1990
Cited by 10
Table 4: Model assumptions for PZT5 actuation of both wings using a pre-stressed unimorph.
2002
Cited by 1
Table 1. NMSE for ANN and FBP reconstruction of the ideal image, the noiseless and the noisy one. Last row is the relative improvement IMP.
1997
"... In PAGE 12: ... (8) The ANN reconstruction for the ideal image (Fig. 10 and Table1 ) is quite perfect because, due to the absence of physical effects, there are no fluctuations between examples proposed to the ANN during training and images presented in the reconstruction phase. We will see that the ANN reconstruction is worse when we introduce physical effects.... In PAGE 12: ... In Fig. 11 we show the reconstruction of the noiseless image: the introduction of physical effects leads to a worse recontruction, both for the FBP and the ANN (see Table1 ). The ANN method is much better, although it is more sensitive to physical effects.... In PAGE 13: ... 12 we show the reconstruction of the noisy image. We can see that now the ANN reconstruction is much worse than in the noiseless case seen above: the improvement now is equal to 66% , but there is still a substantial improvement over the FBP (see Table1 ). The density gradients are better reproduced with the ANN than with the FBP: we see that the FBP is not sensitive to the increase of the statistical noise.... ..."
Table 2: Parameters (ci;n;0, fi;n;0;, i;n;0) of the simulation model (RA pro le) Fig. 4 shows the ideal autocorrelation function ^ r 0(t) = J0(2 fmaxt) in comparison with the resulting autocorrelation function of the simulation model r 0(t) = r 1;0(t) + r 2;0(t) by applying the MCM and the LPNM. On the basis of the parameters listed in Table 2, we have depicted in Fig. 5 the absolute value of the fading channel amplitude (t) = j 0(t)j = q 2 1;0(t) + 2 2;0(t) ; (33) where the simulation time corresponds to a vehicle that covers in a RA environment a distance of 10m by a mobile speed of 110km/h. The probability density function (pdf) of an ideal Rayleigh process, ^ p (x), is given by [13]
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