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Previously, we learned the
concept of independent

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experiments.

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In this exercise, we'll see how
the seemingly simple idea

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of independence can help us
understand the behavior of

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quite complex systems.

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In particular, we'll combined
the concept of independence

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with the idea of divide and
conquer, where we break a

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larger system into smaller
components, and then using

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independent properties to
glue them back together.

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Now, let's take a look
at the problem.

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We are given a network of
connected components, and each

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component can be good with

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probability P or bad otherwise.

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All components are independent
from each other.

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We say the system is operational
if there exists a

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path connecting point A here
to point B that go through

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only the good components.

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And we'd like to understand,
what is the probability that

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system is operational?

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Which we'll denote
by P of A to B.

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Although the problem might seem
a little complicated at

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the beginning, it turns
out only two

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structures really matter.

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So let's look at each of them.

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In the first structure, which we
call the serial structure,

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we have a collection of k
components, each one having

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probability P being good,
connected one next to each

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other in a serial line.

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Now, in this structure, in order
for there to be a good

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path from A to B, every
single one of the

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components must be working.

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So the probability of having
a good path from A to B is

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simply P times P, so on and so,
repeated k times, which is

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P raised to the k power.

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Know that the reason we can
write the probability this

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way, in terms of this product,
is because of the

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independence property.

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Now, the second useful

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structure is parallel structure.

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Here again, we have k components
one, two, through

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k, but this time they're
connected in parallel to each

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other, namely they start from
one point here and ends at

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another point here,
and this holds for

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every single component.

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Now, for the parallel structure
to work, namely for

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there to exist a good path from
A to B, it's easy to see

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that as long as one of these
components works the whole

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thing will work.

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So the probability of A to B
is the probability that at

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least one of these
components works.

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Or in the other word, the
probability of the complement

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of the event where all
components fail.

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Now, if each component has
probability P to be good, then

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the probability that all key
components fail is 1 minus P

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raised to the kth power.

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Again, having this expression
means that we have used the

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property of independence, and
that is probability of having

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a good parallel structure.

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Now, there's one more
observation that will be

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useful for us.

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Just like how we define two
components to be independent,

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we can also find two collections
of components to

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be independent from
each other.

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For example, in this diagram,
if we call the components

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between points C and E as
collection two, and the

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components between E and
B as collection three.

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Now, if we assume that each
component in both

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collections--

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they're completely independent
from each other, then it's not

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hard to see that collection
two and three behave

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independently.

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And this will be very helpful
in getting us the breakdown

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from complex networks
to simpler elements.

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Now, let's go back to the
original problem of

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calculating the probability of
having a good path from point

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big A to point big B
in this diagram.

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Based on that argument of
independent collections, we

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can first divide the whole
network into three

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collections, as you see here,
from A to C, C to E and E to

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B. Now, because they're
independent and in a serial

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structure, as seen by the
definition of a serial

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structure here, we see that the
probability of A to B can

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be written as a probability of
A to C multiplied by C to E,

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and finally, E to B.

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Now, the probability of A to
C is simply P because the

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collection contains
only one element.

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And similarly, the probability
of E to B is not that hard

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knowing the parallel
structure here.

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We see that collection three
has two components in

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parallel, so this probability
will be given by 1 minus 1

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minus P squared.

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And it remains to calculate just
the probability of having

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a good path from point C to
point E. To get a value for P

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C to E, we notice again, that
this area can be treated as

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two components, C1 to E and C2
to E connected in parallel.

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And using the parallel law we
get this probability is 1

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minus 1 minus P C1 to E
multiplied by the 1 minus P C2

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to E. Know that I'm using two
different characters, C1 and

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C2, to denote the same node,
which is C. This is simply for

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making it easier to analyze
two branches where they

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actually do note
the same node.

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Now P C1 to E is another serial
connection of these

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three elements here with
another component.

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So the first three elements are
connected in parallel, and

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we know the probability of that
being successful is 1

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minus P3, and the
last one is P.

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And finally, P C2 to E. It's
just a single element

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component with probability of
successful being P. At this

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point, there is no longer any
unknown variables, and we have

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indeed obtained exact values
for all the quantities that

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we're interested in.

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So starting from this equation,
we can plug in the

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values for P C2 to E, P C1 to E
back here, and then further

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plug in P C to E back here.

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That will give us the final
solution, which is given by

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the following somewhat
complicated formula.

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So in summary, in this problem,
we learned how to use

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the independence property among
different components to

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break down the entire fairly
complex network into simple

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modular components, and use the
law of serial and parallel

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connections to put the
probabilities back together in

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common with the overall success
probability of finding

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a path from A to B.