Why am I getting different results when using the law of total expectation?

In summary, the conversation discusses calculating the expected number of red marbles in a box after 3 selections, given that there was initially 1 red and 1 black marble. Two different methods are used, but one method appears to be incorrect due to not considering the probability of the initial condition. The correct method uses the law of total expectation and considers the probability of the initial condition.
  • #1
adnaps1
4
0
Suppose a box initially contains 1 red marble and 1 black marble and that, at each time n = 1, 2, ..., we randomly select a marble from the box and replace it with one additional marble of the same color. Let X_n denote the number of red marbles in the box at time n (note that X_0 = 1). What is E(X_3)?

In solving this problem, I would like to calculate E(X_3 | X_2 = 1). If there is 1 red marble at time 2 (X_2 = 1), that means the first 2 selections resulted in black marbles. So at the time of the third selection, there are 3 black marbles in the box and 1 red marble. Therefore, E(X_3 | X_2 = 1) = 1(3/4) + 2(1/4) = 5/4 (that is, 1 with probability 3/4 and 2 with probability 1/4).

However, if I would like to calculate E(X_3 | X_2 = 1) differently, using the law of total expectation, I can write E(X_3 | X_2 = 1) = E(X_3 | X_2 = 1, X_1 = 1) P(X_1 = 1). (There are no other values of X_1 to condition on, because if we know there is 1 red marble at time n = 2, there cannot be 2 red marbles at time n = 1.) However, this simplifies to [1(3/4) + 2(1/4)](1/2) = 5/8.

Why am I getting different results? I think the problem has something to do with the following: When I condition on X_1 = 1, I already know X_1 = 1 with probability 1 because X_2 = 1; however, then I say P(X_1 = 1) = 1/2, which is also true.
 
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  • #2
adnaps1 said:
However, if I would like to calculate E(X_3 | X_2 = 1) differently, using the law of total expectation, I can write E(X_3 | X_2 = 1) = E(X_3 | X_2 = 1, X_1 = 1) P(X_1 = 1).

Shouldn't that be E(X_3 | X_2 = 1) = E(X_3 | X_2 =1, X_1=1) P(X_1=1 | X_2 = 1) ?
 
  • #3
Yes, you're right. Thank you very much.
 

FAQ: Why am I getting different results when using the law of total expectation?

What is the "Law of Total Expectation"?

The Law of Total Expectation states that the expected value of a random variable can be calculated by taking the sum of the expected values of its conditional distributions, weighted by their respective probabilities.

Why is the "Law of Total Expectation" important in probability and statistics?

The Law of Total Expectation allows us to calculate the expected value of a complex random variable by breaking it down into simpler conditional distributions. This is useful in various fields such as finance, economics, and engineering, where we often deal with multiple variables and their relationships.

How is the "Law of Total Expectation" used in real-world applications?

The Law of Total Expectation is used in many real-world applications, such as forecasting stock prices, predicting weather patterns, and estimating insurance premiums. It allows us to make informed decisions based on the expected value of a variable, taking into account all possible outcomes.

What is the difference between the "Law of Total Expectation" and the "Law of Total Probability"?

The Law of Total Expectation deals with the expected value of a random variable, while the Law of Total Probability deals with the probability of an event. The former is used to calculate the expected value of a complex random variable, while the latter is used to calculate the probability of an event given multiple possible outcomes.

Can the "Law of Total Expectation" be applied to continuous random variables?

Yes, the Law of Total Expectation can be applied to both discrete and continuous random variables. In the case of a continuous random variable, the sum is replaced by an integral, and the probabilities are represented by a probability density function.

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