Page 369 - Applied Probability
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preorder traversal, 208
Gamete competition model, 126
rooted, 205
unrooted, 206
Gamete probability
Exclusion probability, see Pater-
in terms of avoidance proba-
bilities, 260
Exon, 341 nity testing Gamete, 2 Index 359
under count-location model,
Expected information, see Infor- 261
mation, expected under Poisson-skip model, 265–
Exponential distribution, 310 269
Exponential family, 40 Gauss-Newton algorithm, 53
score and information for spe- Gel electrophoresis, 345
cific examples, 42 pulsed-field, 347
Expressed sequence tags, 347 reading errors, 192
Extinction of allele, 10 Gene, 1
Extreme value distribution, 314 marker, see Marker gene
moments, 315 Gene assignment, using somatic
cell hybrids, 301–304
Factor analysis, 151–152 Gene counting for allele frequen-
example, 152 cies, 22
maximum likelihood, 151 Genetic databases, 281
standard errors, 163 Genetic drift, 12
Fast Walsh transform, 190 Genetic identity coefficient, see Iden-
Felsenstein’s map function, see Map tity coefficient
function, Felsenstein’s Genome, human, 342
Felsenstein’s pulley principle, 215 Genotype, 1
Finger ridge counts, 149–150 multilocus, 4
FISH, see Fluorescence in situ hy- ordered and unordered, 2
bridization Genotype elimination algorithm,
FISHER pedigree analysis software, 118–120, 133
xi, 147 used to find legal descent graph,
Fisher’s exact test, 69 185
Fisher-Yates distribution, 67–69 Genotyping errors, 191–192
moments of, 75 Geometric distribution
Fitness, 9 descent graph transitions, 184
Fixed point, 10, 18 distance between restriction
Fluorescence in situ hybridization, sites, 282
347 sequence matching, 310
Forward algorithm, see Baum’s for- Gibbs random fields, 219
ward algorithm partition function, 220, 227
rate variation model, 220 potential function, 220
Founder, 3 Gibbs sampling, 174, 196
Founder effect, 12 Graph
Founder tree graph, 177–180 descent, see Descent graph
connected component of, 178 number of triangles in, 312
