Notes
Outline
EE391 - Probability Theory
We are dealing here with mass phenomena and their “average” behavior.  The physical system that creates the OBSERVABLE phenomena is usually assumed to be CONSISTENT and the observation {either over time (a sequence) or among a simultaneous population (a SET, an ENSEMBLE)} will have statistics that are also consistent.  We need a theory that describes and predicts these statistics.
Approaches
Physical
Experiment (frequency ratio, apostiori)
Classical Definition (# of trials large)
Conceptual - Axiomatic
Axioms and Reasoning (heavy math)
Prediction
Symmetry (thought experiment, apriori)
The Sample Space
Sample Space
S is the collection or set of all possible outcomes of an experiment.  It is the universal set for this experiment.
Discrete Sample Space:  Finite or countably infinite set.  (e.g. faces of a die, the positive integers, students in this class)
Continuous Sample Space: not countable.  (e.g. the real line, battery voltage in a car)
The Probability Function
Definition
To each outcome in S we associate a non-negative number Pn = P(xn) such that :
0<Pn<1 and Sum[ P(xn)] = 1
xn is in S
Sum is over all elements of S
{xn} is collectively exhaustive and mutually exclusive
Truths
P(A) is greater than or equal to 0
P(S) = 1
P(A + B) = P(A) + P(B) - P(A*B)
P({}) = 0
Mutually Exclusive Events:
P(A + B) = P(A) + P(B)
The three axioms of probability