Induced Measure: Understanding the Concept

In summary, an induced measure is a probability distribution that is obtained by considering the values of a random variable as a new sample space. This concept can be illustrated with examples such as tossing a coin multiple times or throwing darts at a dartboard. The induced measure is defined on the values of the random variable and gives probabilities for each possible outcome.
  • #1
peter.a
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What is an induced measure?
I have seen the formal definition many times i am trying to get a grasp of this concept.

Does an induced measure mean that we can view the measure associated with a random variable as some co-ordinate function defined on R?

Is it the cdf?
 
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  • #2
Since you have seen the formal definition, let me try to illustrate with a few examples.

You toss a coin three times. The sample space is composed of things that look like HTH. There are 8 elements, and if the coin is fair they all have equal probability. Let X be the random variable that sums the numbers of heads. Now the possible values of X are 0,1,2,3. The induced measure is defined on the values of X and gives values of 1/8, 3/8,3/8,1/8 respectively.

Ok, now suppose the experiment is to throw darts at a dartboard, and let's assume for simplicity that they always hit the board and stick. Then there is some probability distribution defined on the disk that tells the likelihoods of hitting the various points. Now let X be the distance from the bull's eye. The induced measure here is a probability measure on [0,R], where R is the radius of the board.

If you have an experiment with sample space S, and then you have a random variable X, the induced measure is the probability distribution you get when you think of the values of X as the new sample space. Of course, there is a much more precise definition, but you have read that so I won't repeat it.
 

1. What is induced measure?

Induced measure is a concept in measure theory that refers to the process of defining a measure on a subset of a larger set. This is done by using a function to map the larger set to the subset, and then using the measure on the larger set to calculate the measure of the subset.

2. Why is induced measure important?

Induced measure is important because it allows us to extend the concept of measure to subsets of a larger set, which may not have a natural measure defined on them. This is useful in many areas of mathematics, such as in probability theory and analysis.

3. How is induced measure different from the usual measure?

The usual measure is defined on a set itself, while induced measure is defined on a subset of a larger set. Induced measure also takes into account the function used to map the larger set to the subset, whereas the usual measure does not.

4. What is the relationship between induced measure and Lebesgue measure?

Lebesgue measure is a specific type of measure that is commonly used in analysis and measure theory. Induced measure can be seen as a generalization of Lebesgue measure, as it allows us to define measures on subsets of a larger set using a function, whereas Lebesgue measure is only defined on subsets of the real numbers.

5. How is induced measure used in real-world applications?

Induced measure has many applications in real-world problems, particularly in probability theory. For example, it is used to calculate the probability of events in a sample space, where the sample space is a subset of a larger set of outcomes. It is also used in statistics to define measures on subsets of a larger data set.

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