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Merge pull request #415 from QuantEcon/update_lln_clt
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[lln_clt] Update editorial suggestions
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jstac authored Apr 9, 2024
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Expand Up @@ -78,7 +78,9 @@ print(X)
```

In this setting, the LLN tells us if we flip the coin many times, the fraction
of heads that we see will be close to the mean $p$.
of heads that we see will be close to the mean $p$.

We use $n$ to represent the number of times the coin is flipped.

Let's check this:

Expand Down Expand Up @@ -286,7 +288,7 @@ as expected.

Let's vary `n` to see how the distribution of the sample mean changes.

We will use a violin plot to show the different distributions.
We will use a [violin plot](https://intro.quantecon.org/prob_dist.html#violin-plots) to show the different distributions.

Each distribution in the violin plot represents the distribution of $X_n$ for some $n$, calculated by simulation.

Expand Down Expand Up @@ -357,7 +359,7 @@ This means that the distribution of $\bar X_n$ does not eventually concentrate o

Hence the LLN does not hold.

The LLN fails to hold here because the assumption $\mathbb E|X| = \infty$ is violated by the Cauchy distribution.
The LLN fails to hold here because the assumption $\mathbb E|X| < \infty$ is violated by the Cauchy distribution.

+++

Expand Down Expand Up @@ -438,7 +440,7 @@ Here $\stackrel { d } {\to} N(0, \sigma^2)$ indicates [convergence in distributi

The striking implication of the CLT is that for **any** distribution with
finite [second moment](https://en.wikipedia.org/wiki/Moment_(mathematics)), the simple operation of adding independent
copies **always** leads to a Gaussian curve.
copies **always** leads to a Gaussian(Normal) curve.



Expand Down Expand Up @@ -503,7 +505,7 @@ The fit to the normal density is already tight and can be further improved by in
```{exercise}
:label: lln_ex1
Repeat the simulation [above1](sim_one) with the [Beta distribution](https://en.wikipedia.org/wiki/Beta_distribution).
Repeat the simulation [above](sim_one) with the [Beta distribution](https://en.wikipedia.org/wiki/Beta_distribution).
You can choose any $\alpha > 0$ and $\beta > 0$.
```
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