The outcome could be any element in the sample space , but the range of possibilities is restricted due to partial information.
Partial Information: Insufficient or fuzzy information about the output
Example: examples of partial information
- The roll of the dice is at least a 4
- The final grade for ELEC 321 is at least 75%
Conditioning Event: The event that represents partial information. The event of interest is denoted by
Example: rolling a dice with conditional event and event of interest
(event of interest)
Example: the final grade of ELEC 321
(event of interest)
Suppose that the probability of is not 0: , then,
This reads “the probability of given equals to the probability of and divided by the probability of .
Rearrange and we get useful formulas:
is a function of and for fixed (otherwise the axioms doesn’t hold) satisfies all of the probability axioms listed in module 1.
If are disjoint for then:
Example: dice roll from above (and assuming each side of the dice is equally likely)
Example: ELEC 321 grades
We suppose that (each percentage is equally likely).
Consider a screening test for defective iPhones, the screening test can either result in:
But screening test itself sometimes have two types of errors:
Given these outcomes, there are total of 4 possible events for each event:
For iPhone status:
For test result:
In this scenario, we will arbitrarily define the sensitivity (probability of test positive given that the iPhone is defective) of the test to be 0.95. Which also implies that the probability of test negative given a defective iPhone is .
We will also arbitrarily define the specificity (probability of test negative given that the iPhone is not defective) of the test to be 0.99. Similarly, the probability that the test is positive if the device is not defective is .
The proportion of the the defective items is also known.
Given the conditions, we can compute:
Probability that a randomly chosen iPhone tests positive
Probability of defective given that the test resulted positive
Probability of defective given that the test resulted negative
Probability of screening error
Bayes’ theorem is a formula that describes how to update the probability of hypothesis given some evidence.
Where is hypothesis, is the evidence.
The simple form of Bayes’ formula is:
How did we get to the expression on the right? which . The denominator can be broken down intuitively into . And then we turn the intersections in the denominator into conditional probability form.
The general form of Bayes’ formula is:
This is identical to above except some conditions need to be satisfied:
Example: three prisoners
- Prisoner A, B, and C are to be executed
- One of the prisoners are randomly chosen by the governor to be pardoned
- Warden knows who is pardoned, but can’t disclose
- Prisoner begs the warden to know which one of the other prisoner is not pardoned
- If B is pardoned, C’s name is given; if C is pardoned, B’s name is given; if A is pardoned, B or C’s name is given (chosen by a random coin flip)
- Warden tells A: “B is not pardoned”
Result: Given the information, C is now twice more likely to be pardoned than A, why?
so we can say that the probability of each prisoner pardoned is . These are the Prior Probability. This also implies that are all disjoint, which satisfies the conditions for the general Bayes’ formula.
Since we safely assume that the warden never lies, we can list the conditional probability of given each of events . The probability of given (probability of warden saying prisoner B is not pardoned while prisoner B is pardoned) is 0. Next, the probability of given prisoner A being pardoned is because of the random coin toss. Last, the probability of given prisoner C being pardoned is 1.
Now we may use Bayes’ formula to compute
… and .
The probability of C being pardoned, thus, is proved to be twice the probability of A pardoned.
Example: Screening Example II
Also let , so suppose
Given these information, we may ask:
- Probability of positive test
- Probability that () is defective when the test resulted positive
- Probability both components are defective when the test resulted positive
- Probability of testing error
Probability of positive test:
Probability of defective:
Probability of positive test:
Events and are independent if the probability of the intersection of and equals to the product of the probability of each.
If and are independent, then
If , then is independent of all .
If and are non-trivial and mutually exclusive () then they cannot be independent.
If then they cannot be independent.
If and are dependent, the probability of can still be calculated.
Consider a system in system and parallel.
The reliability of the system is the rate the of getting a correct output given an input.
And we assume , , and are independent. so
Example: consider that each of the component , , or have the reliability of .
In series, the reliability is:
In parallel, we compute the reliability by computing the contrary (when the system will fail):
are conditionally independent given the event if:
Let be the outcome of -th test:
The outcome will evolve as we obtain more information. is evaluated with no historical information or it is given.
where are outcome as a sequence. and the ‘data’ at step is
So assume that are independent given some event and also given , then for :
This means that the new probability depends on which is the previous probability, and . is the new piece of data. is the intersection of and all previous data ().
Example: pseudo code
Outcomes for the tests:
Probability of event of interest :
Sensitivity of test:
Specificity of test:
Example: demine whether a component of a device is fault or not, base on experiments
Example: whether if a patient has cancer or not
Example: Spam e-mail detection