Rewrite intro tutorial to add uncertainty (#439)

odow · Jul 4, 2021 · 1b9244a · 1b9244a
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diff --git a/docs/src/assets/enso_markovian.png b/docs/src/assets/enso_markovian.png
diff --git a/docs/src/assets/hazard_decision.png b/docs/src/assets/hazard_decision.png
diff --git a/docs/src/tutorial/basic/01_first_steps.jl b/docs/src/tutorial/basic/01_first_steps.jl
diff --git a/docs/src/tutorial/basic/02_adding_uncertainty.jl b/docs/src/tutorial/basic/02_adding_uncertainty.jl
diff --git a/docs/src/tutorial/basic/03_objective_uncertainty.jl b/docs/src/tutorial/basic/03_objective_uncertainty.jl
@@ -1,6 +1,6 @@
 # # Uncertainty in the objective function
 
-# In the previous tutorial, [Basic II: adding uncertainty](@ref), we created a
+# In the previous tutorial, [An introduction to SDDP.jl](@ref), we created a
 # stochastic hydro-thermal scheduling model. In this tutorial, we extend the
 # problem by adding uncertainty to the fuel costs.
 

diff --git a/docs/src/tutorial/basic/04_markov_uncertainty.jl b/docs/src/tutorial/basic/04_markov_uncertainty.jl
@@ -1,9 +1,9 @@
 # # Markovian policy graphs
 
-# In our three tutorials ([An introduction to SDDP.jl](@ref), [Basic II: adding
-# uncertainty](@ref), and [Uncertainty in the objective function](@ref)), we
-# formulated a simple hydrothermal scheduling problem with stagewise-independent
-# noise in the right-hand side of the constraints and in the objective function.
+# In our previous tutorials ([An introduction to SDDP.jl](@ref) and
+# [Uncertainty in the objective function](@ref)), we formulated a simple
+# hydrothermal scheduling problem with stagewise-independent random variables in
+# the right-hand side of the constraints and in the objective function.
 # Now, in this tutorial, we introduce some *stagewise-dependent* uncertainty
 # using a Markov chain.
 

diff --git a/docs/src/tutorial/basic/06_warnings.jl b/docs/src/tutorial/basic/06_warnings.jl
@@ -144,7 +144,7 @@ SDDP.train(model, iteration_limit = 5, run_numerical_stability_report = false)
 # - The bound converges to a value above (if minimizing) the simulated cost of
 #   the policy. In this case, the problem is deterministic, so it is easy to
 #   tell. But you can also check by performing a Monte Carlo simulation like we
-#   did in [Basic II: adding uncertainty](@ref).
+#   did in [An introduction to SDDP.jl](@ref).
 #
 # - The bound converges to different values when we change the bound. This is
 #   another clear give-away. The bound provided by the user is only used in the