maximum likelihood lecture

[maanasee]

Fixes #117

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[jstac]

@maanasee you've done a great job so far!

Let's add the derivation of the maximum likelihood estimators for the lognormal case.

Also, I suggest that we change the exponential distribution estimate into an exercise.

1. First we tell people the max likelihood estimator.
2. Then we ask them to compute total tax revenue.
3. Then we ask them to check the fit and see if it's good or not (using a plot).

So nothing new needs to be added --- it's just a reorganization.

@maanasee Are you able to take care of this in the next few days? Or would you like help?

[maanasee]

Hi @jstac thanks!

I'll be fine working on this, will update the PR by tomorrow!

[jstac]

Thanks @maanasee , nice work, much appreciated :-)

I'll take it from here.

[jstac]

@maanasee , just so you know, you need a blank line before and after $$.

Example:

The function is

$$ f(x) = 2x $$

This function is differentiable...

[jstac]

@maanasee , some of the text is not well formed:

I think it's best if you build your lecture using Jupyter Book to make sure it looks OK. @HengchengZhang , would you mind to help @maanasee with this?

[jstac]

@maanasee , I recommend you use $\ell$ (\ell) instead of $l$ in maths expressions. It's harder to confuse with $1$.

[jstac]

Thanks @HengchengZhang . I see we are both pushing commits. I hope there was no merge problems.

(I actually meant that I'd like you to help @maanasee understand how to view the lecture she is working on using Jupyter Book (build locally and look at the html file in _build) if she's not sure how to do it. But I appreciate you jumping in to help.)

[HengchengZhang]

Thanks @jstac, I've resolved the conflicts already and just made minor edits.

And please feel free to pin me @maanasee whenever you meet some building issues.

[jstac]

Thanks @HengchengZhang , much appreciated.