Cryptography Reference
In-Depth Information
the ratio of the number of ways of success at getting 7 out of 12 tails divided
by the total number of possible outcomes.
In a more general scenario, we could define probability to be a map from a
subset of the power set satisfying certain closure properties. However, to keep
the description simple, we will stick with the power set, which we call the sample
space , and the subsets of the power set are called events , as well as outcomes .
Now suppose that we have two experiments S and T with random events
S
and
T
. Then we can put them together and speak about the joint random
events
). For instance, suppose that we have a standard deck of 52
cards and S consists of the event the value of the card , while
U
=(
S
,
T
T
consists of the
event the suit of the card . Then
) represents all the possibilities of the
52 outcomes of choosing a card. If p s,t represents the probability that a card is
drawn with value s and suit t , then given a fair deck with p s =1 / 13, p t =1 / 4,
and p s,t =1 / 52.
In general, we let p s,t denote the probability that s
U
=(
S
,
T
S
and t
T
both
occur. It follows that
p s =
t T
p s,t ,
so the probability of a fixed s
S
occurring is the sum of all the probabilities
of t
T
occurring along with s
S
occurring.
Independence
The two random events s
S
and t
T
, are called independent if
p s,t = p s ·
p t .
(E.1)
For instance, in the deck of cards illustration above, the suit and value of
the cards are independent events.
Conditional Probability
If we know that event s
S
has occurred, then the probability that t
T
will
occur given that p ( s ) > 0, is defined as follows:
p t | s = p s,t
p s ,
(E.2)
called the conditional probability of t given s .
Notice that if we combine (E.1)-(E.2), we get that
s and t are independent events if and only if p t | s = p t .
In other words, the probability that t occurs is unaffected by the probability
that s occurs.
In what follows, we assume that s and s
are event subsets of
S
.
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