Probability
Document Sample


Probability
Our Goals
Develop a representation for illustrating the
probabilities and outcomes of a particular
scenario
Practice this representation
Investigate a complex scenario to illustrate
the power of probability
The Setting
You are…
Blindfolded,
Led into a room,
Instructed to throw a dart at the far wall
until you are told to stop,
Compute the probability of the possible
outcomes if…
You stop when you hit a balloon
You stop when you hit a balloon
You stop when you hit a balloon
You stop when you hit a balloon
You stop when you hit a balloon
You stop when you hit a square
You win You lose
nothing $10
You win $8
You stop when you hit a square
Win $10 Lose $3
Win $5
You stop when you hit a square
+$25 -$15 +$40
-$10 -$20 +$10
+$5 -$15
Computing the Probability
Heads on Tails on
Flip 1 Flip 1
Heads on
Flip 2
Tails on
Flip 2
Computing the Probability
Roll on First Die
Pr(Sum>9) 1 2 3 4 5 6
Pr(Prod is even)
1
2
Roll on
Second 3
Die
4
5
6
2 Draws with Replacement
3 Red Balls First Draw
4 Green Balls
1 Blue Balls
Second
Draw
2 Draws without Replacement
3 Red Balls First Draw
4 Green Balls
1 Blue Balls
Second
Draw
Area Model
Notice we can use area to see when we
multiply or add probabilities
We can even deal with dependent events
As we get more familiar, we don’t need to
be too accurate with scale- so long as we
keep track of what the probabilities are
An Application of Probabilities
Your demographic has a low occurrence of
AIDS, about 1 in 100,000.
You take a test for AIDS that is 99.9%
accurate, and it comes back positive
Should you be worried? Why or why not?
What if a second (independent) test came
back positive?
Scratch Work
Generalizing
We’ve now seen how the area model can be
used to help compute the probabilities of a
sample space (especially if the space is
comprised of two distinct events)
Heads Tails
on on
Flip 1 Flip 1
Heads on
Flip 2
Tails on
Flip 2
Generalizing
So how might we try to adjust the area
model so that it can succinctly represent
more than two distinct events?
Example: Use the area model to help you
compute the probability of flipping atleast 2
heads in 3 coin flips
Generalizing
Example: Use the area model to help you
compute the probability of flipping atleast 2
heads in 3 coin flips
Heads Tails
on on
Flip 1 Flip 1 HT
HH TT
Heads on
Flip 2
Tails on
Flip 2
One way to generalize
The first two flips
HH TH TT
H
3rd
Flip T
Pr(>1 H)= Pr(exactly 1 H)=
Pr(<3 T)= Pr(3H)=
Computing only one probability
If you are interested in only one particular
outcome, it is typically easier to compute
only that probability and not model the
entire scenario.
Let us consider some examples…
What is the probability that…
Exactly 3 heads occur after 4 consecutive
coin flips.
– How many possible outcomes are there?
– How many possible ways are there for there to
be exactly 3 heads?
Note: Counting can be reinterpreted as the
number of choices one has (Recall the
Cartesian product model of multiplication in
302A)
What is the probability that…
Exactly 2 heads occur after 5 consecutive
coin flips.
– How many possible outcomes are there?
– How many possible ways are there for there to
be exactly 2 heads?
Birthday Problem
Task: Compute the probability that at least
two people in this room have the same
birthday, Pr(shared).
Birthday Problem
Hint: Consider an simpler probability to
compute that is related, namely: what is the
probability that no one has a shared
birthday. Pr(none shared)
Question: If we can compute this
probability, how do we find the original
probability we were asked about?
Summary
Probability of an outcome in a scenario is the
number of times the particular outcome can occur
divided by the total number of all outcomes.
Expected value represents the expected average
value of the scenario as it is repeated many times.
We can use an area model to represent the
probabilities of a scenario
Sometimes the probability that an outcome does
not occur is easier to compute then the probability
that it does.
Homework 5
Exploration 7.18 in the Red Book
– Parts I, II, V
Get documents about "