Let $(X_n)_{n \in \Bbb N}$ be a sequence of positive i.i.d. random variables

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In summary, "i.i.d." stands for "independent and identically distributed," meaning that each random variable in a sequence is independent and follows the same probability distribution. An example of i.i.d. random variables is rolling a fair die multiple times. The expected value of a sequence of i.i.d. random variables can be calculated by multiplying the expected value of a single random variable by the number of variables in the sequence. Having a sequence of positive i.i.d. random variables is significant because it allows for the calculation of moments and provides insights into the behavior of the sequence. I.i.d. random variables can also be used to model real-world phenomena, but it is important to note that there may be dependencies and variations in the
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Euge
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Here is this week's POTW:

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Let $(X_n)_{n \in \Bbb N}$ be a sequence of positive i.i.d. random variables such that $E[\ln X_n]$ is a constant finite positive number $\mu$. Show that if $$T_n := \prod_{i = 1}^n X_i^{1/n}\quad (n = 1,2,3,...)$$ then $(T_n)_{n\in \Bbb N}$ converges in probability to $e^{\mu}$.
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No one answered this week's problem. You can read my solution below.

Note $$T_n = \exp\left(\frac{1}{n}\sum_{i = 1}^n \ln X_n\right)$$ and by the weak law of large numbers, $$\frac{1}{n}\sum_{i = 1}^n\ln X_i \rightarrow \mu\quad \text{in probability}$$ Since, in addition, the map $x\mapsto e^x$ is continuous, then $T_n \to e^\mu$ in probability.
 

FAQ: Let $(X_n)_{n \in \Bbb N}$ be a sequence of positive i.i.d. random variables

What does "i.i.d." stand for in a sequence of random variables?

"i.i.d." stands for "independent and identically distributed." This means that each random variable in the sequence is independent of each other and follows the same probability distribution.

Can you give an example of a sequence of i.i.d. random variables?

Yes, an example of a sequence of i.i.d. random variables is rolling a fair die multiple times. Each roll is independent of the previous roll and follows the same probability distribution of a uniform distribution with six possible outcomes.

How do you calculate the expected value of a sequence of i.i.d. random variables?

The expected value of a sequence of i.i.d. random variables can be calculated by taking the expected value of a single random variable and multiplying it by the number of variables in the sequence. This is because each random variable has the same probability distribution and expected value.

What is the significance of having a sequence of positive i.i.d. random variables?

Having a sequence of positive i.i.d. random variables is significant because it allows for the calculation of the moments of the distribution, such as the mean, variance, and higher moments. This can provide important insights into the behavior and characteristics of the sequence.

Can i.i.d. random variables be used to model real-world phenomena?

Yes, i.i.d. random variables can be used to model real-world phenomena. For example, stock prices are often modeled as a sequence of i.i.d. random variables. However, it is important to note that in reality, there may be dependencies and variations in the probability distribution, so it is not a perfect representation.

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