Examples and further patterns
A4-1 sums of independent random variables
Suppose
Y
N is an independent, integrable sequence. Set
.
If
, then
X
N is a (S)MG.
A4-2 products of nonnegative random variables
Suppose
. Consider
.
If
, then
is a (S)MG
and
. Hence,
A4-3 discrete random walk
Consider
and
iid. Set
. Suppose
. Let
Now
.
Hence,
has at most two roots, one of which is
.
-
is a minimum point iff
, in which case
X
N is a MG (see
A4-1 )
- If
for
, then
.
Let
.
By A4-2,
Z
N is a MG
For the MG case in
Theorem IXA3-6 , the
Y
n are centered at conditional
expectation; that is
The following is an
extension of that pattern.
A4-4 more general sums
Consider integrable
and bounded
.
Let
a constant for
and
for
. Set
Then
is a MG.
and
IXA4-2
A4-5 sums of products
Suppose
Y
N is absolutely fair relative to
Z
N , with
, fixed
. For
, set
Then
is a MG,
, where
We consider, next, some relationships with
homogeneous Markov sequences .
Suppose
is a homogeneous Markov sequence with finite
state space
and transition matrix
.
A function
f on
E is represented by a
column matrix
. Then
has value
when
.
is an
column matrix and
is the
j th
element of that matrix. Consider
. Now
A nonnegative function
f on
E is called
(super)harmonic for
P iff
.
A4-6 positive supermartingales and superharmonic functions.
Suppose
is a homogeneous Markov sequence with finite
state space
and transition matrix
.
For nonnegative
f on
E , let
. Then
is a positive (super)martingale P(SR)MG iff
f is
(super)harmonic for
P .
As noted above
.
- If
f is (super)harmonic
, so that
- If
is a P(SR)MG, then
IX A4-3
An
eigenfunction
f and associated
eigenvalue
λ for
P satisfy
(i.e.,
). In most cases,
. For
real
λ ,
, the eigenfunctions are superharmonic functions. We
may use the construction of
Theorem IXA3-12 to obtain the associated MG.
A4-7 martingales induced by eigenfunctions for homogeneous markov sequences
Let
be a homogenous Markov sequence, and
f be an
eigenfunction with eigenvalue
λ . Put
.
Then, by
Theorem IAXA3-12 ,
is a MG.
A4-8 a dynamic programming example.
We consider a
horizon of N stages and a finite state space
.
-
Observe the system at prescribed instants
- Take
action on the basis of previous states and actions.