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Special Lecture: Conditional Probability Don’t forget to sign in for credit! Example of Conditional Probability in the real world: This chart is from a report from the CA Dept of Forestry and Fire Prevention. It shows the probability of a structure being lost in a forest fire given its location in El Dorado county. (calculated using fuel available, land slope, trees, neighborhood etc.)

Special Lecture: Conditional Probability

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Special Lecture: Conditional Probability. Example of Conditional Probability in the real world: This chart is from a report from the CA Dept of Forestry and Fire Prevention. It shows the probability of a structure being lost in a forest fire given its location in El Dorado - PowerPoint PPT Presentation

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Page 1: Special Lecture: Conditional Probability

Special Lecture: Conditional Probability

Don’t forget to sign in for credit!

Example of Conditional Probability in the real world:

This chart is from a report from the CA Dept of Forestry and Fire Prevention.It shows the probabilityof a structure beinglost in a forest fire given itslocation in El Dorado county. (calculated usingfuel available, land slope, trees, neighborhood etc.)

Page 2: Special Lecture: Conditional Probability

The Plan…Today, I plan to cover material

related to these ALEKS topics.

Specifically, we’ll…• Review all the formulas we’ll

need.• Go over one conceptual

example in depth.• Work through a number of the

ALEKS problems that have been giving you trouble.

• Address any specific questions/problems.

Page 3: Special Lecture: Conditional Probability

Formulas:Event Probability Terms/Explanation

A p(A) [0,1] probability of A is between 0 and 1

Not A p(A’) = 1 - p (A) Compliment: Note that the probability of either getting A or not getting A sums to 1.

A or B (or both)

p(AB) = p(A) + p(B)-p(AB)=p(A) + p(B)

Union:

if A & B are mutually exclusive

A & B p(AB) = p(A)p(B) = p(A|B)p(B)

Intersection: only if A and B are independent

A given B

P(A|B) = p(AB)/p(B) Conditional Probability: The probability of event A given that you already have event B.

Page 4: Special Lecture: Conditional Probability

Bayes’ Theorem: This is simply derived from what we already know about conditional probability.

Formulas:

p(A|B) = p(B|A)*p(A) p(B)

Or if we don’t have p(B) we can use the more complicated variation of Bayes’:

p(A|B) = p(B|A)*p(A) p(B|A)*p(A) +p(B|A’)*p(A’)

The reason those two formulas are the same has to do with the Law of Total Probabilities:

For any finite (or countably infinite) random variable,

p(A) = ∑ p(ABn)or, p (A) = ∑ p(A|Bn)p(Bn)

Page 5: Special Lecture: Conditional Probability

Formulas: All together nowEvent Probability Terms/Explanation

A p(A) [0,1]p (A) = ∑ p(ABn) = ∑ p(A|Bn)p(Bn)

probability of A is between 0 and 1And is the sum of all partitions of A

Not A p(A’) = 1 - p (A) Compliment: probability of either getting A or not getting A sums to 1.

A or B (or both)

p(AB) = p(A) + p(B)-p(AB)=p(A) + p(B)

Union: only if A & B are mutually exclusive

A & B p(AB) = p(A)p(B) = p(A|B)p(B) = p(B|A)p(A)

Intersection: only if A and B are independent

A given B

P(A|B) = p(AB)/p(B) = p(B|A)p(A)/p(B) = p(B|A)p(A) p(B|A)*p(A) +p(B|A’)*p(A’)

Conditional Probability: The probability of event A given that you already have event B.

Page 6: Special Lecture: Conditional Probability

Shapes DemoImagine that we have the following population of shapes:

Notice that there are several dimensions that we could use to sort or group these shapes:

• Shape• Color • Size

We could also calculate the frequency with which each of these groups appears and determine the probability of randomly selecting a shape with a particular dimension from the larger set of shapes.

So let’s do that…

Page 7: Special Lecture: Conditional Probability

Shapes Demo

• P(R)• P(Y)• P(B)

= 8/24 = 1/3= 8/24 = 1/3= 8/24 = 1/3

Imagine that we have the following population of shapes:

• P( )• P( )• P( )• P( )

= 6/24 = 1/4= 6/24 = 1/4= 6/24 = 1/4= 6/24 = 1/4

• P(BIG)• P(small)

= 12/24 = 1/2= 12/24 = 1/2

Page 8: Special Lecture: Conditional Probability

• P(R)• P(Y)• P(B)

= 8/24 = 1/3= 8/24 = 1/3= 8/24 = 1/3

• P( )• P( )• P( )• P( )

= 6/24 = 1/4= 6/24 = 1/4= 6/24 = 1/4= 6/24 = 1/4

• P(BIG)• P(small)

= 12/24 = 1/2= 12/24 = 1/2

Now that we’ve figured out the probability of these events,What else can we do?

Page 9: Special Lecture: Conditional Probability

• P(R)• P(Y)• P(B)

= 8/24 = 1/3= 8/24 = 1/3= 8/24 = 1/3

• P( )• P( )• P( )• P( )

= 6/24 = 1/4= 6/24 = 1/4= 6/24 = 1/4= 6/24 = 1/4

• P(BIG)• P(small)

= 12/24 = 1/2= 12/24 = 1/2

Now that we’ve figured out the probability of these events,What else can we do? Lots of stuff!

What’s the probabilityof getting a blue triangle?

= p(B )

= 8/24 * 6/24 = 48/576 = 2/24 = 1/12

= p(B)*p( ) p( )

Page 10: Special Lecture: Conditional Probability

• P(R)• P(Y)• P(B)

= 8/24 = 1/3= 8/24 = 1/3= 8/24 = 1/3

• P( )• P( )• P( )• P( )

= 6/24 = 1/4= 6/24 = 1/4= 6/24 = 1/4= 6/24 = 1/4

• P(BIG)• P(small)

= 12/24 = 1/2= 12/24 = 1/2

Now that we’ve figured out the probability of these events,What else can we do? Lots of stuff!

What else?

= p(B ) = 1/12 p( )

p( or B or ) = p(B )

= p(B )+p( )- p(B ) = 8/24 +6/24 - 1/12 =12/24 =1/2

Page 11: Special Lecture: Conditional Probability

• P(R)• P(Y)• P(B)

= 8/24 = 1/3= 8/24 = 1/3= 8/24 = 1/3

• P( )• P( )• P( )• P( )

= 6/24 = 1/4= 6/24 = 1/4= 6/24 = 1/4= 6/24 = 1/4

• P(BIG)• P(small)

= 12/24 = 1/2= 12/24 = 1/2

Now that we’ve figured out the probability of these events,What else can we do? Lots of stuff!

What else?

= p(B ) = 1/12 p( )

p( or B or ) = p(B )=1/2

p( given that we have B)

= p(B ) /p(B) = 2/24 / 8/24 = 2/8 = 1/4

= p( |B)

Page 12: Special Lecture: Conditional Probability

So, the calculations work out…

But do they make sense??

Page 13: Special Lecture: Conditional Probability

How to approach ALEKS problems

1. Write down everything you know.2. Write down (and probably draw out) what

you need to figure out.3. Figure out a plan.4. Go.

Page 14: Special Lecture: Conditional Probability

So, Let’s Try an ALEKS problem.

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Any other questions or concerns?