Merge branch 'master' into gh/update_manifests

TuringLang · Mar 1, 2024 · c488feb · c488feb
2 parents 3dad399 + 8515a56
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diff --git a/tutorials/10-bayesian-differential-equations/10_bayesian-differential-equations.jmd b/tutorials/10-bayesian-differential-equations/10_bayesian-differential-equations.jmd
@@ -436,7 +436,7 @@ chain_sde = sample(
     model_sde,
     NUTS(0.25),
     5000;
-    init_params=[1.5, 1.3, 1.2, 2.7, 1.2, 0.12, 0.12],
+    initial_params=[1.5, 1.3, 1.2, 2.7, 1.2, 0.12, 0.12],
     progress=false,
 )
 plot(chain_sde)

diff --git a/tutorials/docs-12-using-turing-guide/using-turing-guide.jmd b/tutorials/docs-12-using-turing-guide/using-turing-guide.jmd
@@ -493,14 +493,14 @@ Standard errors are calculated from the Fisher information matrix (inverse Hessi
 
 #### Sampling with the MAP/MLE as initial states
 
-You can begin sampling your chain from an MLE/MAP estimate by extracting the vector of parameter values and providing it to the `sample` function with the keyword `init_params`. For example, here is how to sample from the full posterior using the MAP estimate as the starting point:
+You can begin sampling your chain from an MLE/MAP estimate by extracting the vector of parameter values and providing it to the `sample` function with the keyword `initial_params`. For example, here is how to sample from the full posterior using the MAP estimate as the starting point:
 
 ```julia; eval=false
 # Generate an MAP estimate.
 map_estimate = optimize(model, MAP())
 
 # Sample with the MAP estimate as the starting point.
-chain = sample(model, NUTS(), 1_000; init_params=map_estimate.values.array)
+chain = sample(model, NUTS(), 1_000; initial_params=map_estimate.values.array)
 ```
 
 ## Beyond the Basics