Rename mle to mle_intro so lecture names are unique across the published sites

lectures/_config.yml

@@ -147,6 +147,7 @@ sphinx:
       index_toc.md: intro.md
       lake_model.md: lake_model_intro.md
       lln_clt.md: lln_clt_intro.md
+      mle.md: mle_intro.md
     # Remote Redirects
     redirects:
       ak2:   https://python.quantecon.org/ak2.html

lectures/_toc.yml

@@ -25,7 +25,7 @@ parts:
   numbered: true
   chapters:
   - file: simple_linear_regression
-  - file: mle
+  - file: mle_intro
   - file: wealth_tax
   - file: bayes_intro
 - caption: Foundations

lectures/fitting_distributions.md

@@ -45,7 +45,7 @@ make the fit as close as possible.
 This lecture is mainly about the first part.
 
 For the second we use just one technique, called the method of moments, leaving
-a fuller treatment to {doc}`mle`.
+a fuller treatment to {doc}`mle_intro`.
 
 Even so, we start with the parameters, since we have to be able to fit a class
 before we can judge it.
@@ -736,7 +736,7 @@ Our estimate of it came from the sample kurtosis, which is a fourth moment, and
 higher moments are estimated poorly precisely when the tails are heavy.
 
 Fitting this distribution by maximum likelihood instead, as we do in
-{doc}`mle`, gives $\nu \approx 3.6$ rather than $5.8$, and a smaller KS distance
+{doc}`mle_intro`, gives $\nu \approx 3.6$ rather than $5.8$, and a smaller KS distance
 again.
 
 The method of moments is simple and general, but it is not always the best use

lectures/wealth_tax.md

@@ -40,7 +40,7 @@ survey data.
 Estimation is challenging because the richest households are under-represented in the data.
 
 To fill the gap we model the upper tail of the wealth distribution with a
-Pareto distribution, which we fit by {doc}`maximum likelihood <mle>`.
+Pareto distribution, which we fit by {doc}`maximum likelihood <mle_intro>`.
 
 We will use the following imports.
 
@@ -282,7 +282,7 @@ which is also where our tax begins.
 
 ## Estimating the tail index
 
-In {doc}`mle` we found that the maximum likelihood estimate of the tail index,
+In {doc}`mle_intro` we found that the maximum likelihood estimate of the tail index,
 given observations $x_1, \ldots, x_n$ above a known threshold $u$, is
 
 $$
@@ -298,7 +298,7 @@ $$
 \ell(\alpha) = \sum_{i: w_i > u} \lambda_i \ln f(w_i; \alpha)
 $$
 
-Repeating the calculation in {doc}`mle` with these weights gives
+Repeating the calculation in {doc}`mle_intro` with these weights gives
 
 $$
 \hat \alpha = \frac{\sum_{i: w_i > u} \lambda_i}