8th February 2010
Summaries
Session 1.7
Marginal, Joint and Conditional
Probabilities
Suppose that we have fair dice:
d4 with face values
The first stage probabilities:
Pr
Pr
Pr
The conditional probabilities:
Pr
Pr
Pr
Pr
Pr
Pr
Pr
Pr
Pr
Pr
Pr
Pr
Pr
Pr
Pr
Pr
Pr
Pr
The joint probabilities
Pr
Pr
Pr
Pr
Pr
Pr
Pr
Pr
Sample Tables
Data Pending
Conditional Probability
Conditional = Joint / Prior
Pr
How much of B is tied up in A ?
Case Study 1.11
Conditional Probability
Case Study Description: Compute conditional probabilities associated with the color sequence experiment.
Suppose that we have a special box - each time we press a button on the box, it prints out a sequence of colors, in order - it prints four colors at a time. Suppose the box follows the following Probabilities for each Color Sequence:
Color Sequence |
Probability CS Prints Out |
BBBB |
.10 = 10% |
BGGB |
.25 = 25% |
RGGR |
.05 = 05% |
YYYY |
.30 = 30% |
BYRG |
.15 = 15% |
RYYB |
.15 = 15% |
Total |
1.00 = 100% |
Let's define the experiment: We push the button, and then the box prints out exactly one (1) of the above listed color sequences. We then note the resulting (printed out) color sequence.
Compute
Pr
Pr
Pr
Pr
So, Pr
Compute
Pr
Pr
Pr
So, Pr
Compute
Pr
Pr
Pr
Pr
So, Pr
Case Study 1.12
Conditional Probability II: Pair of Dice
Case Description: Compute conditional probabilities.
Suppose we have a pair of fair dice: d4(faces 1,2,3,4), d6(faces 1,2,3,4,5,6). In our experiment, we toss this pair of dice, and note the face value from each die. For simplicity, we write the outcome as (d4 result, d6 result). Assume that the dice operate independently and separately.
Case Objectives:
Identify the simple (basic) events. Compute (and justify) a probability for each simple event.
As before, Pr
We have 24 equally likely pairs.
|
1 |
2 |
3 |
4 |
1 |
(1,1) |
(2,1) |
(3,1) |
(4,1) |
2 |
(1,2) |
(2,2) |
(3,2) |
(4,2) |
3 |
(1,3) |
(2,3) |
(3,3) |
(4,3) |
4 |
(1,4) |
(2,4) |
(3,4) |
(4,4) |
5 |
(1,5) |
(2,5) |
(3,5) |
(4,5) |
6 |
(1,6) |
(2,6) |
(3,6) |
(4,6) |
Suppose we observe the sum of the faces in the pair of dice. Identify the possible values of this sum, and compute (and justify) a probability for each value.
Now for the sums:
|
1 |
2 |
3 |
4 |
1 |
(1,1) @ 2 |
(2,1) @ 3 |
(3,1) @ 4 |
(4,1) @ 5 |
2 |
(1,2) @ 3 |
(2,2) @ 4 |
(3,2) @ 5 |
(4,2) @ 6 |
3 |
(1,3) @ 4 |
(2,3) @ 5 |
(3,3) @ 6 |
(4,3) @ 7 |
4 |
(1,4) @ 5 |
(2,4) @ 6 |
(3,4) @ 7 |
(4,4) @ 8 |
5 |
(1,5) @ 6 |
(2,5) @ 7 |
(3,5) @ 8 |
(4,5) @ 9 |
6 |
(1,6) @ 7 |
(2,6) @ 8 |
(3,6) @ 9 |
(4,6) @ 10 |
Compute
the conditional probability Pr
Pr
Pr
Pr
So, Pr
Continuing,…
|
1 |
2 |
3 |
4 |
1 |
(1,1) @ 2 |
(2,1) @ 3 |
(3,1) @ 4 |
(4,1) @ 5 |
2 |
(1,2) @ 3 |
(2,2) @ 4 |
(3,2) @ 5 |
(4,2) @ 6 |
3 |
(1,3) @ 4 |
(2,3) @ 5 |
(3,3) @ 6 |
(4,3) @ 7 |
4 |
(1,4) @ 5 |
(2,4) @ 6 |
(3,4) @ 7 |
(4,4) @ 8 |
5 |
(1,5) @ 6 |
(2,5) @ 7 |
(3,5) @ 8 |
(4,5) @ 9 |
6 |
(1,6) @ 7 |
(2,6) @ 8 |
(3,6) @ 9 |
(4,6) @ 10 |
Compute
the conditional probability Pr
Pr
Pr
Pr
So, Pr
HR1 – Summer Version A, Case
Three
Case Three | Color
Slot Machine | Conditional Probabilities
Here is our slot machine – on each trial, it produces a 10-color sequence, using the table below:
Sequence* |
Probability |
RRBBRRYRRR |
.10 |
RRGGRGBRRB |
.10 |
BBYYGGYGBR |
.15 |
GRRGGYBRGG |
.10 |
BGYGYRYGYY |
.25 |
RRYYGRRBBY |
.10 |
YYGBYYBGRR |
.20 |
Total |
1.00 |
*B-Blue, G-Green, R-Red, Y-Yellow, Sequence is numbered from left to right: (1st 2nd 3rd 4th 5th6th7th 8th 9th 10th )
Compute the following
conditional probabilities:
Pr
Sequence* |
Probability |
RRBBRRYRRR |
.10 |
RRGGRGBRRB |
.10 |
BBYYGGYGBR |
.15 |
GRRGGYBRGG |
.10 |
BGYGYRYGYY |
.25 |
RRYYGRRBBY |
.10 |
YYGBYYBGRR |
.20 |
Total |
1.00 |
Pr
Pr
Sequence* |
Probability |
|
|
Total |
0 |
Pr
Pr
Pr
Sequence* |
Probability |
RRBBRRYRRR |
.10 |
RRGGRGBRRB |
.10 |
BBYYGGYGBR |
.15 |
GRRGGYBRGG |
.10 |
Total |
.45 |
Pr
Pr
Pr
Sequence* |
Probability |
RRGGRGBRRB |
.10 |
BBYYGGYGBR |
.15 |
GRRGGYBRGG |
.10 |
Total |
.35 |
Pr
Pr
Pr
Pr
Sequence* |
Probability |
RRGGRGBRRB |
.10 |
BBYYGGYGBR |
.15 |
GRRGGYBRGG |
.10 |
BGYGYRYGYY |
.25 |
RRYYGRRBBY |
.10 |
YYGBYYBGRR |
.20 |
Total |
.90 |
Pr
Pr
Pr
Sequence* |
Probability |
RRGGRGBRRB |
.10 |
BBYYGGYGBR |
.15 |
GRRGGYBRGG |
.10 |
BGYGYRYGYY |
.25 |
RRYYGRRBBY |
.10 |
YYGBYYBGRR |
.20 |
Total |
.90 |
Pr
Pr
Pr
HR1 – Spring
2008, Case Four
Case Four: Color Slot Machine, Computation of Conditional
Probabilities
Here is our slot machine – on each
trial, it produces a 10-color sequence, using the table below:
Sequence* |
Probability |
RRBBR RYRRB |
.10 |
RRGGRGBRRB |
.10 |
BBYYRGYGBR |
.15 |
GRRGRGBRGB |
.10 |
BGYGYRYGYY |
.25 |
RRGYGRRBBB |
.10 |
YYGBYYBGRR |
.20 |
Total |
1.00 |
*B-Blue, G-Green, R-Red, Y-Yellow, Sequence is numbered as
1st to 6th , from left to right: (1st 2nd
3rd 4th 5th6th7th 8th
9th 10th )
Compute the following conditional probabilities:
1. Pr
Pr
Pr
Sequence* |
Probability |
RRBBR RYRRB |
.10 |
BBYYRGYGBR |
.15 |
BGYGYRYGYY |
.25 |
Total |
0.50 |
Pr
Pr
Sequence* |
Probability |
RRBBR RYRRB |
.10 |
Total |
0.10 |
Pr
Pr
2. Pr
Pr
Pr
Sequence* |
Probability |
RRBBR RYRRB |
.10 |
RRGGRGBRRB |
.10 |
RRGYGRRBBB |
.10 |
Total |
0.30 |
Pr
Sequence* |
Probability |
RRGGRGBRRB |
.10 |
RRGYGRRBBB |
.10 |
Total |
0.20 |
Pr
Pr
3. Pr
Pr
Pr
Sequence* |
Probability |
RRBBR RYRRB |
.10 |
RRGGRGBRRB |
.10 |
BBYYRGYGBR |
.15 |
GRRGRGBRGB |
.10 |
BGYGYRYGYY |
.25 |
RRGYGRRBBB |
.10 |
YYGBYYBGRR |
.20 |
Total |
1.00 |
Pr
Sequence* |
Probability |
RRBBR RYRRB |
.10 |
BBYYRGYGBR |
.15 |
BGYGYRYGYY |
.25 |
RRGYGRRBBB |
.10 |
YYGBYYBGRR |
.20 |
Total |
0.80 |
Pr
Pr
Case Study 1.13
Conditional Probability
Case Description: Compute conditional probabilities for pairs of draws (without replacement).
Here is our bowl, in tabular form:
Color |
# in Bowl |
Proportion of Bowl |
Blue |
5 |
5/9 |
Green |
3 |
3/9 |
Red |
1 |
1/9 |
Total |
9 |
1 |
Suppose that on each trial of this experiment that we make two (2) draws without replacement from the bowl.
Compute
Pr
Here is our bowl, after "red shows 1st", in tabular form:
Color |
# in Bowl – Before 1st Draw |
# in Bowl – After 1st Draw |
Blue |
5 |
5 – 0 = 5 |
Green |
3 |
3 – 0 = 3 |
Red |
1 |
1 – 1 = 0 |
Total |
9 |
8 |
With the red chip out of the bowl, 3 of the 8 surviving
chips are green. So, Pr
Compute
Pr
Pr
Compute
Pr
Here is our bowl, after "blue shows 1st", in tabular form:
Color |
# in Bowl – Before 1st Draw |
# in Bowl – After 1st Draw |
Blue |
5 |
5 – 1 = 4 |
Green |
3 |
3 – 0 = 3 |
Red |
1 |
1 – 0 = 1 |
Total |
9 |
8 |
Pr
HR1 – Fall 2004, Case Three
Case Three
Conditional Probability
Color Bowl/Draws without
Replacement
We have a bowl containing the
following colors and counts of balls (color@count):
Blue @ 5, Green @ 1, Red @ 2, Yellow @ 3
Each trial of our experiment consists
of three (3) draws without replacement from the bowl.
Compute these directly.
Color |
Count |
B |
5 |
G |
1 |
R |
2 |
Y |
3 |
Total |
11 |
Pr
Color |
Count |
B |
5 |
G |
1 |
R |
2 |
Y |
3 |
Total |
11 |
ß green shows 1st
Color |
Count |
B |
5 |
G |
0 |
R |
2 |
Y |
3 |
Total |
10 |
Pr
Pr
Color |
Count |
B |
5 |
G |
1 |
R |
2 |
Y |
3 |
Total |
11 |
ß green shows 1st
Color |
Count |
B |
5 |
G |
0 |
R |
2 |
Y |
3 |
Total |
10 |
ß blue shows 2nd
Color |
Count |
B |
4 |
G |
0 |
R |
2 |
Y |
3 |
Total |
9 |
Pr
Pr
Color |
Count |
B |
5 |
G |
1 |
R |
2 |
Y |
3 |
Total |
11 |
ß green shows 1st
Color |
Count |
B |
5 |
G |
0 |
R |
2 |
Y |
3 |
Total |
10 |
ß red shows 2nd
Color |
Count |
B |
5 |
G |
0 |
R |
1 |
Y |
3 |
Total |
9 |
Pr