readme tweaks

README.md

@@ -21,13 +21,13 @@ A self-contained C99 library that provides a subset of [Squiggle](https://www.sq
 You can follow some example usage in the examples/ folder
 
 1. In the [1st example](examples/01_one_sample/example.c), we define a small model, and draw one sample from it
-2. In the 2nd example, we define a small model, and return many samples
-3. In the 3rd example, we use a gcc extension—nested functions—to rewrite the code from point 2. in a more linear way.
-4. In the 4th example, we define some simple cdfs, and we draw samples from those cdfs. We see that this approach is slower than using the built-in samplers, e.g., the normal sampler.
-5. In the 5th example, we define the cdf for the beta distribution, and we draw samples from it. 
-6. In the 6th example, we take samples from simple gamma and beta distributions, using the samplers provided by this library.
-7. In the 7th example, we get the 90% confidence interval of a beta distribution
-8. The 8th example translates the models from Eli and Nuño from [Samotsvety Nuclear Risk Forecasts — March 2022](https://forum.nunosempere.com/posts/KRFXjCqqfGQAYirm5/samotsvety-nuclear-risk-forecasts-march-2022#Nu_o_Sempere) into squiggle.c, then creates a mixture from both, and returns the mean probability of death per month and the 90% confidence interval.
+2. In the [2nd example](examples/02_many_samples/example.c), we define a small model, and return many samples
+3. In the [3rd example](examples/03_gcc_nested_function/example.c), we use a gcc extension—nested functions—to rewrite the code from point 2. in a more linear way.
+4. In the [4th example](examples/04_sample_from_cdf_simple/example.c), we define some simple cdfs, and we draw samples from those cdfs. We see that this approach is slower than using the built-in samplers, e.g., the normal sampler.
+5. In the [5th example](examples/05_sample_from_cdf_beta/example.c), we define the cdf for the beta distribution, and we draw samples from it. 
+6. In the [6th example](examples/06_gamma_beta/example.c), we take samples from simple gamma and beta distributions, using the samplers provided by this library.
+7. In the [7th example](examples/07_ci_beta/example.c), we get the 90% confidence interval of a beta distribution
+8. The [8th example](examples/08_nuclear_war/example.c) translates the models from Eli and Nuño from [Samotsvety Nuclear Risk Forecasts — March 2022](https://forum.nunosempere.com/posts/KRFXjCqqfGQAYirm5/samotsvety-nuclear-risk-forecasts-march-2022#Nu_o_Sempere) into squiggle.c, then creates a mixture from both, and returns the mean probability of death per month and the 90% confidence interval.
 
 ## Commentary
 
@@ -258,11 +258,10 @@ Overall, I would caution that if you really care about the very far tails of dis
 
 ## To do list
 
-- [ ] Link to the examples in the examples section.
 - [ ] Systematize references
 - [ ] Publish online
 - [ ] Support all distribution functions in <https://www.squiggle-language.com/docs/Api/Dist>
-- [ ] Support all distribution functions in <https://www.squiggle-language.com/docs/Api/Dist>, and do so efficiently
+  - [ ] do so efficiently
 
 ## Done
 
@@ -307,3 +306,4 @@ Overall, I would caution that if you really care about the very far tails of dis
 - [x] Give warning about sampling-based methods.
 - [x] Have some more complicated & realistic example
 - [x] Add summarization functions: 90% ci (or all c.i.?) 
+- [x] Link to the examples in the examples section.

examples/01_one_sample/example.c

@@ -41,11 +41,3 @@ int main(){
 		printf("result_one: %f\n", result_one);
 		free(seed);
 }
-
-/* 
-Aggregation mechanisms:
-- Quantiles (requires a sort)
-- Sum 
-- Average
-- Std
-*/

examples/02_many_samples/example.c

@@ -50,11 +50,3 @@ int main(){
 		printf("]\n");
 		free(seed);
 }
-
-/* 
-Aggregation mechanisms:
-- Quantiles (requires a sort)
-- Sum 
-- Average
-- Std
-*/