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Notation:
 
Notation:
  
1) Event: A, B, C...
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1) Sample space (S): Set of all possible outcomes of an experiment (e.g., S={O1,O2,..,On}).
  
2) Probability of an event to occur: P(A), P(B), P(C)...
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2) Event: Any subsets of outcomes (e.g., A={O1,O2,Ok}) contained in the sample space S.
  
 
Formula for probability:
 
Formula for probability:

Revision as of 12:45, 10 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. Probability can range from 0 to 1, where 0 means the event to be an impossible one and 1 indicates a certain event. The probability of all the events in a sample space adds up to 1.

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:



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

Bayes derivation.png

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:

Total prob.png

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:

Total prob ex.png

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.

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