Page 372 - Applied Probability
P. 372
Index
362
convergence for three loci, 16
on pedigree descent graphs,
convergence for two loci, 8
convergence when generations
175–177, 181–184
transition rules, 181–183, 198
overlap, 16
with multiple transitions per
nonparametric test for, 74
testing for, 67–69 improving mixing of, 196–197
step, 184
Location scores, 131 restriction site model, 172, 195
stochastic method for, 175, used in Poisson-skip model,
184, 187 265
Locus, 1 Markov chain Monte Carlo, 169–
Lod score, 130 198
Log-concavity for testing linkage equilibrium,
in count-location model, 271, 69
277 Marriage theorem, see Hall’s mar-
in Poisson-skip model, 271 riage theorem
of ABO likelihood, 52 Mather’s formula, see Recombi-
nation fraction
Map function, 261 Matrix exponential, 210, 223
Carter and Falconer’s, 275 Maximum likelihood estimation
Felsenstein’s, 275 by scoring, see Scoring method
for Poisson-skip model, 265 for evolutionary trees, 214–
for radiation hybrid breakage, 215
232 compared to maximum par-
Haldane’s, 131 simony, 216
Karlin’s, 274 for radiation hybrid mapping
Kosambi’s, 264, 275 haploid case, 236, 251
properties of, 262 polyploid case, 240
Mapping, linkage, see Linkage analy- in factor analysis, see Factor
sis analysis
Marker(s), 4 Newton’s method, see New-
gaps between, 304–306 ton’s method
short tandem repeat, 347 of allele frequencies, 32–33
Marker-sharing statistics, 112, 192– of power-series parameter, 53
195 Quasi-Newton methods, see
affecteds-only method, 107 Quasi-Newton methods
for dominant diseases, 193 using EM algorithm, see EM
for recessive diseases, 193 algorithm
simulation of p-values, 194 variances and covariances, 40
Markov chain Maximum parsimony, 205–208, 223
continuous time, 209–210 algorithm for, 206
average number of transi- compared to maximum like-
tions, 224 lihood, 216
equilibrium distribution, 226 tree traversal scheme, 208
discrete time, 170–173 Mean components, 141
on DNA strand, 172 Meiosis, 2
