Difference between revisions of "Swanson MA279 Fall2022 topic4" - Rhea

Revision as of 10:58, 15 November 2022

Probability : Group 4

Kalpit Patel Rick Jiang Bowen Wang Kyle Arrowood

This is an edit

Probability, Conditional Probability, Bayes Theorem Disease example

Definition: Probability is a measure of the likelihood of an event to occur. Many events cannot be predicted with total certainty. We can predict only the chance of an event to occur i.e., how likely they are going to happen, using it.

Notation:

1) Sample space (S): Set of all possible outcomes of an experiment (e.g., S={O1,O2,..,On}).

2) Event: Any subsets of outcomes (e.g., A={O1,O2,Ok}) contained in the sample space S.

Formula for probability:

1. Let N:= |S| be the size of sample space

2. Let N(A):= |A| be the number of simple outcomes contained in event A

3. Then P(A) = N(A)/N

Mutually exclusivity:

When events A and B have no outcomes in common

Axioms of probability

Baye's Theorem Derivation: Starting with the definition of Conditional Probability

In Step 1 with the definition of conditional probabilities, it is assumed that P(A) and P(B) are not 0.

In Step 2 we multiply both sides by P(B) and in step 3 we multiply both sides by P(A) and we end up with the same result on the right hand side of each.

In Step 4 we are able to set both left sides equal to each other since they both have the same right hand side.

In Step 5 we divide both sides by P(B) and we arrive at Baye's Theorem.

It is also important to introduce the law of total probability:

The law of total probability is commonly used in Baye's rule examples in order to calculate the denominator. A quick example using the law of total probability is:

In this example we are able to solve for the probability of A by just using the probability of A given B, the probability of A given the complement of B, and the probability of B.