Question about probability measure

In summary, a probability measure on a space \Omega is a function that satisfies three axioms: non-negativity, additivity for disjoint sets, and a value of 1 for the entire space. If M is a probability measure, we can show that the function M/2 satisfies axioms (i) and (ii) but not (iii). However, the function M^2 satisfies (i) and (iii) but not necessarily (ii). A counterexample for (ii) is when the space \Omega = {A, B} and the probability measure M assigns a value of 0 to the entire space, 1/2 to A, 1/2 to B, and 1 to the
  • #1
Alexsandro
51
0
An probability measure on same space [itex]\Omega[/itex] is a function of subsets of [itex]\Omega[/itex] satisfying three axioms:

(i) For every set [itex]A \subset \Omega[/itex], the value of the function is a non-negative number: P(A) [itex]\geqslant[/itex] 0.

(ii) For any two disjoint sets A and B, the value of the function for their union A + B is equal to the sum of its value for A and its value for B:

P(A + B) = P(A) + P(B) provided A.B = [itex]{\O}[/itex].

(iii) The value of the function for [itex]\Omega[/itex] (as a subset) is equal to 1:

P([itex]\Omega[/itex]) = 1.


Now, reply these questions:

If M is a probability measure, show:

(a) that the function M/2 satisfies Axiom(i) and (ii) but not (iii).

(b) the function [itex]M^2[/itex] satisfies (i) and (iii) but not necessary (ii); give a counterexample to (ii).
 
Physics news on Phys.org
  • #2
We can't help if you if you haven't tried it.
 
  • #4
  • #5
Omega={A,B}
M(gurnisht)=0
M({A})=1/2
M({B})=1/2
M({A,B})=1
then
M^2({A,B})!=M^2({A})+M^2({B})
(Either it's that simple or i don't understand the problem)
 

What is a probability measure?

A probability measure is a mathematical function that assigns a numerical value between 0 and 1 to a set of outcomes in a probability space. It is used to quantify the likelihood of an event occurring.

How is a probability measure different from a probability distribution?

A probability measure is a mathematical function that assigns probabilities to sets of outcomes, while a probability distribution is a function that assigns probabilities to individual outcomes. In other words, a probability measure describes the overall likelihood of an event occurring, while a probability distribution describes the likelihood of each possible outcome.

What is the difference between discrete and continuous probability measures?

A discrete probability measure is one in which the possible outcomes are countable and can be represented by a finite or countably infinite set of values. A continuous probability measure is one in which the possible outcomes are uncountable and can be represented by a range of real numbers.

How is a probability measure calculated?

A probability measure is calculated by dividing the number of favorable outcomes by the total number of possible outcomes. This can be represented as a fraction, decimal, or percentage.

What is the importance of probability measures in scientific research?

Probability measures are essential in scientific research as they allow us to quantify the likelihood of an event occurring and make informed decisions based on that likelihood. They are used in various fields such as statistics, physics, and biology to model and analyze complex systems and make predictions about future outcomes.

Similar threads

  • Introductory Physics Homework Help
Replies
9
Views
1K
  • Introductory Physics Homework Help
Replies
2
Views
613
  • Introductory Physics Homework Help
Replies
2
Views
786
  • Introductory Physics Homework Help
Replies
19
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
2
Views
1K
  • Introductory Physics Homework Help
Replies
4
Views
2K
Replies
1
Views
897
  • Introductory Physics Homework Help
Replies
28
Views
347
  • Introductory Physics Homework Help
Replies
6
Views
472
Replies
12
Views
724
Back
Top