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particles of weights w1 and w2 have the same distribution of possible random walks For any particular random walk, particle 2 s score is w2 /w1 times particle 1 s score (Note that this assumption in transport is akin to the assumption in the previous section speci ed by the phrase otherwise has no in uence on the sample path of the chain after step j ) Under this assumption, a particle s score is directly proportional to its weight and the rth score moment for a particle of weight w is wr times the rth score moment for a unit weight particle [22, p 163] To give some idea of the ubiquitousness of this assumption, note that [22] rst mentions this assumption in a footnote A cautionary note is perhaps worthwhile here Because weight-independent simulations are more tractable mathematically, they account for almost all of the theoretical discussions in the Monte Carlo literature (Two good exceptions can be found in [22, pp 178 and 186]) One should not be misled into concluding that weight-independent simulations are more important, better, or more widely used than weight-dependent simulations Many (probably most) of the large production Monte Carlo codes allow weight-dependent simulation MCNP has always done weight-dependent simulation as a default (To the author s knowledge, the predecessor codes to MCNP, as far back as the 1950s, always did weight-dependent simulation as a default as well) There is often some distance between Monte Carlo theory and practice Two examples are given below First, consider the weight window technique (described later in more detail) that enforces a range of acceptable weights by splitting if the weight is above the window and rouletting if the weight is below the window After the enforcement, all weights are then within the window The weight window is perhaps the most widely used variance reduction technique in transport Monte Carlo today, but it has received scant theoretical attention (Fox [19, pp 213 233] gives an interesting discussion) Second, consider Monte Carlo optimization techniques There are numerous theoretical derivations about optimal parameters to minimize the variance; they almost always assume weight-independent transport A favorite problem for theorists is optimizing the exponential transform [26, 27, 18, 24] (described later) (The reference list is not exhaustive; see [22, p 487] for more) Inasmuch as practical experience indicates that a weight window almost always improves the performance of the exponential transform, the usefulness of optimizing the exponential transform in the absence of a weight window is severely curtailed An empirically optimized transform used with a weight window can give very good results When the weight window is removed with the same transform parameter, the results are often disastrous In one documented case [3, pp 54 56], the ef ciency decreased by a factor of 100 The author s speculation as to the reason for the divergence between theory and practice is that: (i) transport theoreticians focus on techniques that are more mathematically analyzable;. java ean 13 reader Java Barcode Reader Tutorial to scan, read linear, 2d barcodes in ...
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