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Photograph by Orren Jack Turner, Courtesy Princeton University Library.

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WILLIAM FELLER
July 7, 1906–January 14, 1970
By MURRAy ROSENBLATT
W major figures in the devel-
illiam f eller w as o ne o f t he
opment of interest and research in probability theory
in the United States as well as internationally. He was born
in Zagreb, yugoslavia, on July 7, 1906, the son of Eugen
Viktor Feller, a prosperous owner of a chemical factory,
and Ida Perc. Feller was the youngest of eight brothers,
one of twelve siblings. He was a student at the University of
Zagreb (1923-1925) and received the equivalent of a master
of science degree there. Feller then entered the University
of Göttingen in 1925 and completed his doctorate with a
thesis titled “Uber algebraisch rektifizierbare transzendente
Kurven.” His thesis advisor was Richard Courant. He left
Göttingen in 1928 and took up a position as privat dozent at
the University of Kiel in 1928. Feller left in 1933 after refus-
ing to sign a Nazi oath. He spent a year in Copenhagen and
then five years (1934-1939) in contact with Harald Cramér
and Marcel Riesz in Sweden. On July 27, 1938, he married
Clara Nielsen, a student of his in Kiel.
At the beginning of the 20th century the most incisive
research in probability theory had been carried out in France
and Russia. There was still a lack of effective basis for a
mathematical theory of probability. There was a notion of
a collective introduced by von Mises, defined as a sequence
71

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72 BIOGRAPHICAL MEMOIRS
of observations with certain desirable asymptotic properties.
An effective formalization of probability theory was given in
a 1933 monograph of Kolmogorov that was based on mea-
sure theory.1 An attempt to get a rigorous format for the
von Mises collective led in the 1960s to an approach using
algorithmic information theory by Kolmogorov and others.
Feller’s research made use of the measure theoretic founda-
tions of probability theory as did most mathematical work.
Feller’s first published paper in probability theory (1936)
obtained necessary and sufficient conditions for the central
limit theorem of probability theory. This was a culmination
of earlier work of DeMoivre, Laplace, and Liapounov. The
concern is with the asymptotic behavior of the partial sums
Sn=X1+…+Xn of a sequence of independent random variables
X1,X2,…,Xn,…. Assuming that means mk=EXk and variances
B2 =E(S2 )-(ESn)2 exist, Feller showed that the normalized and
n n n
centered sums ∑(Xj-mj)/Bn(with the summands (Xj-mj)/Bn uni-
j=1
formly asymptotically negligible) in distribution converge to
the normal law
u2
1 x-
∫
Φ(x)= √2π −∞e 2du
as n→∞ if and only if the Lindeberg condition
n
1
∑ ∫x dF (x+m )→0
2
2 k k
Bn k=1 |x|>εBn
(Fk is the distribution function of Xk) as n→∞ is satisfied
for each ε>0.
The actual fluctuating behavior of the sequence Sn was
first properly formulated by Borel in 1909. After initial ad-
vances by Hausdorff, Hardy and Littlewood, and Khinchin,
Kolmogorov obtained a law of the iterated logarithm, which

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73
WILLIAM FELLER
states that under some strong boundedness conditions on
the Xk that with probability one
limsup Sn - E(Sn)
=1
n→∞ √2B2nloglogB2n
In a number of papers Feller through much research
during his life extended and improved the law of the iter-
ated logarithm.
A reviewing journal Zeutralblatt für Mathematik had been
set up in 1931. The editor was Otto Neugebauer whose in-
terests were in the history of mathematics and astronomy.
Neugebauer resigned after the Nazi government’s racist
restrictions on reviewers were implemented. The American
Mathematical Society with outside monetary support estab-
lished a mathematics reviewing journal, Mathematical Reviews,
with Neugebauer, Tamarkin, and Feller as effective editorial
staff. The mathematical community is indebted to Feller for
his help in setting up what became the primary mathematical
reviewing journal in the world. Willy and Clara Feller moved
to the United States in 1939 and Feller became an associate
professor at Brown University.
In 1931 a paper of Kolmogorov on analytic methods in
probability theory discussed the differential equations satisfied
by the transition probabilities of continuous time parameter
Markov processes (random processes with the property that
past and future of the process are conditionally independent
given precise knowledge of the present).2 The conditional
probability that a system at time t at location x will at time
τ>t be less than or equal to y is given by a function F(t,x,τ,y).
It was known that in the case of a diffusion the function F
would satisfy a Fokker-Planck or diffusion equation
2
∂F +a(t,x)∂F +b(t,x)∂F =0
∂x 2
∂t ∂t

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74 BIOGRAPHICAL MEMOIRS
in the backward variables t,x, where a(t,x) and b(t,x) repre-
sent the local fluctuation and local drift respectively at x at
time t. Feller, under appropriate growth and smoothness
conditions on a(t,x) and b(t,x) constructed a conditional
distribution function F as a solution of the diffusion equa-
tion and demonstrated its uniqueness in (1937,1). Under
additional conditions on the coefficients a(t,x), b(t,x) the
conditional distribution function F(t,x;τ,y) is shown to have
∂
a density f(t,x;τ,y)= ∂y F(t,x;τ,y) in y and the density satisfies
the formal adjoint differential equation in τ,y.
∂f ∂2
- ∂τ - 2 [a(τ,y)f]+ ∂ [b(τ,y)f]=0
∂y ∂y
The case of purely discontinuous or jump Markov pro-
cesses in continuous time was also examined and under
appropriate conditions an integrodifferential equation is
shown to be satisfied by the constructed transition probabil-
ity function of the Markov process. The more complicated
situation in which one has continuous excursions as well
as jumps was also considered. These results were a strong
extension of those of Kolmogorov.
The paper (1939) is an example of Feller’s continued
interest in applications, here in a biological context. He looks
at the Lotka-Volterra equations, which are a deterministic
predator-prey model as well as a deterministic model of popu-
lation growth (and decrease). In setting up corresponding
Markovian models it is noted that the mean response in the
stochastic models may not agree precisely with the original
deterministic models. A diffusion equation is shown to arise
naturally if population is considered a continuous variable
but an equation that is singular in having the coefficient
a(x) zero at a boundary point.
An amusing and insightful paper (1940,2) of Feller’s
related to some presumed statistical evidence for the exis-

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75
WILLIAM FELLER
tence of extrasensory perception (ESP). Experiments were
carried out involving sequences of fair coin tosses. Track was
kept of the successful versus unsuccessful guesses of the coin
tossing up to time n,Sn. It was noted that there were often
large excursions of Sn above zero as well as large excursions
below zero. A large excursion above zero was interpreted
as presence of ESP and a large excursion below zero as its
departure. Feller incisively notes that the standard model of
fair coin tossing accounts for such excursions without the
extra introduction of special effects like ESP.
Feller moved to Cornell University in 1945 as professor
of mathematics and remained there until 1950, when he left
to go to Princeton University. While at Cornell he wrote the
paper (1948) in which he derived the Kolmogorov-Smirnov
limit theorems by methods of a simpler character than those
used to derive these results originally. The limit theorems
provide procedures for effective one and two sample sta-
tistical tests. An elegant paper (1949,1) written jointly with
Paul Erdös and Harry Pollard used elementary methods to
establish limit behavior of transition probabilities for count-
able state discrete time Markov chains under appropriate
conditions (a result obtained earlier by Kolmogorov using
arguments that were more elaborate). Feller’s paper (1949,2)
on recurrent events also appeared and extended the basic
idea of an argument that often was used in the analysis of
Markov chains and that was perhaps suggested by early work
of Wolfgang Doeblin.
The year 1950 is marked by the publication of the first
volume of Feller’s Introduction to Probability Theory and Its
Applications. The book gives an extended discussion of the
nature of probability theory. To avoid measure theory the
development is limited to sample spaces that are finite or
countable. Results on fluctuation in coin tossing and ran-
dom walks are dealt with. There is the usual discussion of

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76 BIOGRAPHICAL MEMOIRS
conditional probability and stochastic independence. The
binomial and Poisson distributions are introduced together
with the classical deMoivre normal approximation to the
binomial distribution. Definitions of random variables and
expectation are then given. A law of large numbers and a
law of iterated logarithm (for partial sums of 0-1 random
variables) are given as limit laws in the context of finite
sample size with increasing sample size so as to avoid a formal
discussion using a background of measure theory. Branch-
ing processes and compound distributions are introduced
using generating functions as a convenient tool. Recurrent
events (as introduced by Feller) and renewal theory then
follow and are considered in the context of Geiger counters
or the servicing of machines. Stationary transition function
countable state Markov chains are introduced as examples of
dependent sequences. The Chapman-Kolmogorov equation
is noted as a consequence of the Markov property (but not
equivalent to it). The ergodic properties of Markov processes
are then developed. An algebraic treatment is given for finite
state Markov chains. Finally birth and death processes are
introduced and considered as examples of countable state
continuous time parameter processes. The book is remark-
able with its extensive collection of interesting problems and
its discussion of applications.
Feller worked for eight years on the preparation of
this volume. The volume was completed in the last year of
Feller’s tenure at Cornell University. The book is dedicated
to Neugebauer. Gian-Carlo Rota remarked that the book
was “one of the great events in mathematics of this century.
Together with Weber’s Algebra and Artin’s Geometric Algebra
this is the finest textbook in mathematics in this century. It is
a delight to read and it will be immensely useful to scientists
in all fields.”3

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77
WILLIAM FELLER
The academic year 1949-1950 at Cornell University was
a truly remarkable one in probability theory. The permanent
and visiting faculty members were W. Feller, M. Kac, J. L.
Doob, G. Elfving, G. Hunt, and K. L. Chung, and it was a
most stimulating time for students.
I recall some impressions from my own days as a gradu-
ate student in the late 1940s at Cornell, where I took most of
my courses in stochastics with Feller as a lecturer. Though I
completed my doctorate with Mark Kac as adviser, I had an
overwhelming impression of Will Feller as a man of supreme
enthusiasm and occasional exaggeration that at times required
some modification. An amusing example is given by a lecture
where he introduced the three series theorem and turned
to the student audience inquiring, “Isn’t it obvious?” Luck-
ily we persuaded him to give a detailed presentation, and a
series of two or more lectures on the theorem followed. He
did give great insight in his lectures.
In 1950 he took a position at Princeton University as
Eugene Higgins Professor of Mathematics. He held this
position until his death on January 14, 1970, at 63 in the
Memorial Hospital of New york.
The paper (1952,1) is an indication of Feller’s renewed
interest in diffusion processes and their application in ge-
netics. Stochastic processes as models in genetics and the
theory of evolution are developed. Current methods at that
time were due to R. A. Fisher and Sewall Wright for the most
part. It is indicated how in a model of S. Wright the gene
frequency u(t,x) that satisfies a diffusion equation
ut= {βx(1-x)u}xx - {[γ2-(γ1+γ2)x]u}x
is obtained by an appropriate limiting process from a
bivariate discrete model. The equation has singular bound-

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78 BIOGRAPHICAL MEMOIRS
ary points at 0 and 1. Here β, γ1 and γ2 are constants with
the γ’s denoting mutation rates.
In the 1950s Feller carried out his well-known research
on one dimensional diffusion processes with stationary transi-
tion function F(t,x;τ,y)=P(τ-t;x,y). He made use of appropri-
ate modifications of the Hille-yosida theory of semigroups.
The stationary transition function P(t)= P(t;x,y) generates a
semigroup
P(t+s;x,y)= ∫P(t;x,dz)P(s;z,y), t,s≥0
because of the Chapman-Kolmogorov equation. The transition
function can be considered as an operator on appropriate
spaces. Feller found it convenient to make the assumption
that the transition function P(t) acting on the space of con-
tinuous functions f
{P(t)f }(x)= ∫P(t;x,dy)f(y)
takes continuous functions f into continuous functions P(t)f .
Such transition functions are now usually called Feller transi-
tion functions. The derived operator L given by
lim(P(t)f −f )/t=Lf
t↓0
is defined for a subclass of functions f and is in the case
of the one dimensional diffusion the second order opera-
tor in x of the Fokker-Planck equation. A corresponding
Markov diffusion process was determined by boundary con-
ditions that might differ from those conventionally dealt
with in the standard theory of differential equations. The
boundary conditions could be regarded as a restraint on the
class of functions on which L operated that in turn made
the restrained L the infinitesimal generator of a properly

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79
WILLIAM FELLER
defined transition function semigroup. He generalized the
type of differential operator of diffusion theory. Every such
operator (barring certain degenerate cases) could be writ-
ten in the form (d/dμ)(d/dσ), with σ a scale function and μ
an increasing function (or speed measure). In a natural way
the general linear diffusion was related to a Wiener process
(Norbert Wiener’s model of Brownian motion) that was lo-
cally rescaled in space and speed. Aspects of this program
were laid out in the papers (1954; 1959,1). Dynkin carried
out research on a number of related problems.
Feller also carried out analyses of countable state con-
tinuous time Markov chains with stationary transition prob-
abilities. Here the transition probabilities are given by a
matrix-valued function P(t)=(pij(t)) (i,j states of the process)
with the semigroup property P(t)P(s)=P(t+s), t,s ≥0. If P(t) is
differentiable at zero with finite derivatives qij=pʹ (0), the
ij
equalities ∑qij =0 hold. The backward differential equation
j
QP(t)=Pʹ(t) and the forward differential equation P(t)Q= Pʹ(t)
are often referred to as the Kolmogorov differential equations
(Q=(qij)). In (1957,2) Feller described a general method of
constructing transition probability functions P(t) that satisfy
the conditions on the qij’s. It is also shown how to construct
transition functions P(t) that satisfy both systems of differential
equations. Research on topics of this type was also carried
out by J. L. Doob, K. L. Chung, H. Reiter, and D. Kendall.
The second volume of Feller’s Introduction to Probability
Theory and Its Applications appeared in 1966. It was written
so as to be independent of the first volume. Further, it was
aimed to be of interest to a large audience ranging from a
novice to an expert in the area. The book certainly succeeds,
but it understandably could not be as popular as the first
volume. The first few chapters deal with special distributions
like the exponential, the uniform, and the normal. Chapter 4
introduces probability spaces and probability measures. Laws

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80 BIOGRAPHICAL MEMOIRS
of large numbers, the Hausdorff moment problem, and the
inversion formula for Laplace transforms follow in Chap-
ter 7. The central limit theorem and ergodic theorems for
Markov chains are obtained in Chapter 8. Infinitely divisible
distributions follow in the next chapter. A host of additional
topics follow in the remaining chapters of the book: Markov
processes and semigroups, renewal theory, random walks on
the real line, characteristic functions, expansions related to
the central limit theory, the Berry-Esséen theorem on the
error term in the central limit theorem, large deviations, and
aspects of harmonic analysis. Many of the topics are dealt
with in an elegant and succinct manner.
Feller was always interested in the problems of genetics.
Toward the end of his life, as a permanent visiting profes-
sor at Rockefeller University, he had a close collaboration
there with Professor Dobzhansky and colleagues. The paper
(1966,2) is a result of this interaction and corrects an error
in the theory of evolution due to assumption of constant
population size in the case of a two-allele population.
The papers (1968, 1970) show Feller’s continued research
on problems related to the law of the iterated logarithm
continued throughout his life.
Fel ler’s investigations were greatly appreciated. He
was elected to the National Academy of Sciences in 1960
and was a member of the American Philosophical Society,
the American Academy of Arts and Sciences, and a foreign
member of the Danish and yugoslav academies of science.
He was president of the Institute of Mathematical Statistics.
His widow accepted the National Medal of Science on his
behalf in 1970.
J. L. Doob, who was as influential as Feller in nurturing
and developing the growth and interest in probability theory,
remarked,

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81
WILLIAM FELLER
[A]part from his mathematics those who knew him personally will remem-
ber Feller most for his gusto, the pleasure with which he met life, the ex-
citement with which he drew on his endless fund of anecdotes about life
and its absurdities, particularly the absurdities involving mathematics and
mathematicians. To listen to him deliver a mathematics lecture was a unique
experience. . . In losing him, the world of mathematics has lost one of its
strongest personalities as well as one of its strongest researchers.
A colleague of many years at Cornell University and
Rockefeller University, also a remarkable researcher in prob-
ability theory, Mark Kac, said of Feller:
Feller was a man of enormous vitality. . . The intensity of his reactions was
reflected in what his friends called the “Feller factor,” an imprecisely defined
number by which one had to scale down some of his pronouncements to
get near the truth. . . But he was not stubborn and underneath the bluster,
kind and generous. . . Much as he loved mathematics, his view of it was any-
thing but parochial. . . I recall a conversation in which a colleague asked, .
. . “What can the generals do that we mathematicians couldn’t do better?”
“Sleep during battle,” said Feller and that was that. . . When he learned that
his illness was terminal his courage and considerateness came poignantly to
the fore. Having accepted the verdict himself he tried to make it easy for
all of us to accept it too. He behaved so naturally and he took such interest
in things around him that he made us almost forget from time to time that
he was mortally ill. . . One of the most original, accomplished and colorful
mathematicians of our time.
Henry McKean, a student of and co-researcher with
Feller noted:
[H]is enthusiasm, his high standards, his indefatigable desire to make you
understand “what’s really going on.” That was also his watchword when he
lectured. He would get quite excited, his audience in his hand and come
(almost) to the point. Then the hour would be over, and he would prom-
ise to tell us what’s really going on next time. Only next time the subject
would be not quite the same, and so a whole train of things was left hang-
ing, somewhat in the manner of Tristram Shandy. But it didn’t matter. We
loved it and couldn’t wait for the next (aborted) revelation. . . Back to Will
himself. He was short, compact, with a mop of wooly gray hair, irrepressible.
In conversation quick, always ready with an opinion (or two) addicted to

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82 BIOGRAPHICAL MEMOIRS
exaggeration. If you knew the code, you applied the “Feller factor” (discount
by 90%). . . I think of him often, hearing his voice, remembering him so
full of fun.
This memorial is based in part on an obituary in The Annals of Math-
ematical Statistics 1970, vol. 41 and on accounts written by J. L.
Doob and M. Kac in the Proceedings of the 6th Berkeley Symposium on
Mathematical Statistics and Probability. Helpful written remarks of H.
P. McKean have also been used.
NOTES
1. A. Kolmogorov. Grundbegriffe der Wahrscheinlichkeitsrechnung .
Berlin: Springer, 1933.
2. A. Kolmogorov. über die analytischen Methoden in der Wahrs-
cheinlichkeitsrechnung. Math. Ann. 104(1931):415-458.
3. Back Cover Blurb. from W. Feller. An Introduction to Probabil-
ity Theory and Its Applications, vol. 1, 3rd edition. New york: Wiley,
1968

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83
WILLIAM FELLER
SELECTED BIBLIOGRAPHy
1928
Über algebraisch rektifizierbare transzendente Kurven. M ath. Z.
27:481-495.
1936
Über den zentralen Grenzwertsatz der Wahrscheinlichkeitsrechnung.
Math. Z. 40:521-559.
1937
[1] Zur Theorie der stochastischen Prozesse (Existenz und Eindeu-
tigkeitssätze). Math. Ann. 113:113-160.
[2] Über den zentralen Grenzwertsatz der Wahrscheinlichkeitsrech-
nung. II. Math Z. 42:301-312.
1939
Die Grundlagen der Volterraschen Theorie des Kampfes ums Dasein
in wahrscheinlichkeitstheoretischer Behandlung. Acta Biotheor. A
5:11-40.
1940
[1] On the integro-differential equations of purely discontinuous
Markov processes. Trans. Am. Math. Soc. 48:488-515.
[2] Statistical aspects of ESP. J. Parapsychol. 4(2):271-298.
1941
On the integral equation of renewal theory. Ann. Math. Stat. 12:243-
267.
1943
The general form of the so-called law of the iterated logarithm. Trans.
Am. Math. Soc. 54:373-402.
1945
The fundamental limit theorems in probability. Bull. Am. Math. Soc.
51:800-832.

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84 BIOGRAPHICAL MEMOIRS
1946
The law of the iterated logarithm for identically distributed random
variables. Ann. Math. 47:631-638.
1948
On the Kolmogorov-Smirnov limit theorems for empirical distribu-
tions. Ann. Math. Stat. 19:177-189.
1949
[1] With P. Erdös and H. Pollard. A property of power series with
positive coefficients. Bull. Am. Math. Soc. 55:201-204.
[2] Fluctuation theory of recurrent events. Trans. Am. Math. Soc.
67:98-119.
1950
An Introduction to Probability Theory and Its Applications, vol. 1. New
york: Wiley.
1952
[1] Diffusion processes in genetics. Proceedings of the Second Berkeley
Symposium on Mathematical Statistics and Probability, ed. J. Neyman.
pp. 227-246: Berkeley and Los Angeles: University of California
Press.
[2] Some recent trends in the mathematical theory of diffusion.
Proceedings of the International Congress of Mathematicians (2), pp.
322-339. Providence: American Mathematical Society.
1954
The general diffusion operator and positivity preserving semigroups
in one dimension. Ann. Math. 60:417-436.
1957
[1] Generalized second order differential operators and their lateral
conditions. Illinois J. Math. 1:459-504.
[2] On boundaries and lateral conditions for the Kolmogorov dif-
ferential equations. Ann. Math. 65:527-570.

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WILLIAM FELLER
1959
[1] Differential operators with the positive maximum property. Il-
linois J. Math. 3:182-186.
[2] The birth and death process as diffusion process. J. Math. Pure.
Appl. 38:301-345.
1966
[1] An Introduction to Probability Theory and Its Applications, vol. 2.
New york: Wiley.
[2] On the influence of natural selection on population size. Proc.
Natl. Acad. Sci. U. S. A. 55:733-738.
1968
An extension of the law of the iterated logarithm to variables without
variance. J. Math. Mech. 18:343-356.
1970
On the oscillations of the sums of independent random variables.
Ann. Math. 91:402-418.