### Table 1. Potential hydroelectric energy - Posterior summaries for the parameters Multi-scale model AR(1) model

2001

Cited by 2

### Table 2. Northern hemisphere temperatures - Posterior summaries for the parameters Multi-scale model AR(1) model

2001

Cited by 2

### Table 1. Potential hydroelectric energy - Posterior summaries for the parameters Multi-scale model AR(1) model

### Table 2. Northern hemisphere temperatures - Posterior summaries for the parameters Multi-scale model AR(1) model

### Table 1: Computations for multi{scale scheme und original nite volume scheme

### Table 1: Error statistics for #0Cxed-scale and multi-scale techniques for the OFCE based on the error measure quot;

1998

"... In PAGE 22: ... #5B7#5D, which is based on the spatiotemporal orientation of the measured vector v e relative to that of the correct vector v c #28recall that v = #281; u; v#29#29: quot; = arccos#28b v c #01 b v e #29 with b v = v p v #01 v : #2828#29 Although wehave maintained con#0Cdence measures with velocity estimates, wehave used a threshold on the Frobenius norm to discard uncertain vectors. In Table1 the results are listed for various spatiotem- poral scales. The following conclusions can be drawn: #0F First order approximation does not necessarily perform better than zeroth order for #0Cxed scales.... ..."

Cited by 18

### Table 2. Timing results for the single scale and multiscale MDS. The multi-scale scheme signifi- cantly reduces the computation time

2005

Cited by 2

### Table 2.1: Condition numbers of the sti ness matrix K k at scale k for the linear and quadratic nite elements and DGHM multi-scaling function elements.

1998

Cited by 1

### Table 5.1: Results for ETC (W/m-K) for Polycarbonate-Air bed Geom Continuum Models Unit Cell Models Empr Multi-Scale

2006

### Table 3: Comparison of Multi-Scale Oriented Patches and SIFT feature matching. Note that SIFT features have a larger number of matches per interest point for each of the 3 datasets.

2004