文档介绍：Preface to the First Edition
When I was a postdoctoral fellow at UCLA more than two decades ago,
I learned ic modeling from the delightful texts of Elandt-Johnson [2]
and Cavalli-Sforza and Bodmer [1]. In teaching my own ics course over
the past few years, first at UCLA and later at the University of Michigan,
I longed for an updated version of these books. Neither appeared and I was
left to my own devices. As my hastily assembled notes gradually acquired
more polish, it occurred to me that they might fill a useful niche. Research
in mathematical and statistical ics has been proceeding at such a
breathless pace that the best minds in the field would rather create new
theories than take time to codify the old. It is also far more profitable to
write another grant proposal. Needless to say, this state of affairs is not
ideal for students, who are forced to learn by wading unguided into the
confusing swamp of the current scientific literature.
Having set the stage for nobly rescuing a generation of students, let me
inject a note of honesty. This book is not the monumental synthesis of pop-
ulation ics and ic epidemiology achieved by Cavalli-Sforza and
Bodmer. It is also not the sustained integration of statistics and ics
achieved by Elandt-Johnson. It is not even pendium of men-
dations for carrying out a ic study, useful as that may be. My goal
is different and more modest. I simply wish to equip students already so-
phisticated in mathematics and statistics to engage in ic modeling.
These are the individuals capable of creating new models and methods
for analyzing ic data. No amount of expertise in ics can over-
come mathematical and statistical deficits. Conversely, no mathematician
or statistician ignorant of the basic principles of ics can ever hope to
identify worthy problems. Collaborations between icists on one side
and mathematicians and statisticians on the other can work, but it takes
patience and a willingness to learn a foreign vocabul