Prove using three basic probability axioms

In summary, the proof for P(A \cap B)≥1-P(\bar{A})-P(\bar{B}) for all A, B \subseteq S using the given axioms involves rearranging the inequality, using the fact that the first two terms are disjoint, and then noting that the first term is equal to P(S). This proof is incorrect as it starts out by claiming what it is trying to prove instead of arriving at a contradiction.
  • #1
hassman
36
0

Homework Statement


Prove that [itex]P(A \cap B)≥1-P(\bar{A})-P(\bar{B})[/itex]

for all [itex] A, B \subseteq S[/itex]using only these axioms:
1) [itex]0 \leq P(A) \leq 1[/itex], for any event [itex]A \subseteq S[/itex]
2) [itex]P(S) = 1[/itex]
3) [itex] P(A \cup B) = P(A) + P(B)[/itex] if and only if [itex] P(A \cap B) = 0[/itex]

Homework Equations


None.

The Attempt at a Solution



My proof:

First rearrange the shizzle:

[itex]P(\bar{A}) + P(A \cap B) + P(\bar{B}) \geq 1[/itex]

Now using the fact that the first two terms are disjoint, use axiom 3 to obtain:

[itex]P(\bar{A} \cup (A \cap B)) + P(\bar{B}) \geq 1[/itex]

Next note that the first term is equals to P(S), hence we get:

[itex]P(S) + P(\bar{B}) \geq 1 [/itex]

which holds for all [itex] B \subseteq S[/itex], because [itex]P(S) = 1[/itex] and [itex] P(\bar{B}) \geq 0 [/itex] for all B.

Is this proof correct?
 
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  • #2
hassman said:

Homework Statement


Prove that [itex]P(A \cap B)≥1-P(\bar{A})-P(\bar{B})[/itex]

for all [itex] A, B \subseteq S[/itex]


using only these axioms:
1) [itex]0 \leq P(A) \leq 1[/itex], for any event [itex]A \subseteq S[/itex]
2) [itex]P(S) = 1[/itex]
3) [itex] P(A \cup B) = P(A) + P(B)[/itex] if and only if [itex] P(A \cap B) = 0[/itex]


Homework Equations


None.


The Attempt at a Solution



My proof:

First rearrange the shizzle:

[itex]P(\bar{A}) + P(A \cap B) + P(\bar{B}) \geq 1[/itex]

Now using the fact that the first two terms are disjoint, use axiom 3 to obtain:

[itex]P(\bar{A} \cup (A \cap B)) + P(\bar{B}) \geq 1[/itex]

Next note that the first term is equals to P(S), hence we get:

[itex]P(S) + P(\bar{B}) \geq 1 [/itex]

which holds for all [itex] B \subseteq S[/itex], because [itex]P(S) = 1[/itex] and [itex] P(\bar{B}) \geq 0 [/itex] for all B.

Is this proof correct?

In proof you can't start out claiming what you are trying to prove - you can only claim it isn't true and arriving at a contradiction. So you would say

Suppose [itex]P(A \cap B)<1-P(\bar{A})-P(\bar{B})[/itex] then try to arrive at a contradiction.
 
  • #3
Got it! Thanks!
 

1. What are the three basic probability axioms?

The three basic probability axioms are the axiom of non-negativity, which states that the probability of an event cannot be negative, the axiom of total probability, which states that the sum of the probabilities of all possible outcomes in a sample space is equal to 1, and the axiom of additivity, which states that the probability of the union of two mutually exclusive events is equal to the sum of their individual probabilities.

2. How are these axioms used to prove probability statements?

These axioms serve as the foundation of probability theory and are used to derive various properties and theorems in probability. By applying these axioms, we can prove statements about the likelihood of events occurring and make predictions based on these probabilities.

3. Can these axioms be applied to both discrete and continuous probability distributions?

Yes, these axioms can be applied to both discrete and continuous probability distributions. The first axiom, non-negativity, applies to both types of distributions, while the second and third axioms, total probability and additivity, apply specifically to discrete distributions.

4. Are these axioms universally accepted in the field of probability theory?

Yes, these axioms are widely accepted in the field of probability theory and are considered the fundamental principles of probability. They provide a consistent and logical framework for understanding and analyzing uncertain events.

5. Can these axioms be extended to more complex probability problems?

Yes, these axioms can be extended to more complex probability problems by combining them with other mathematical concepts and tools. For example, the axioms can be used in conjunction with combinatorics, calculus, and statistics to solve more advanced probability problems.

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