Merge branch 'master' into wct/improve-ad-docs

TuringLang · Nov 8, 2024 · 5e03a1e · 5e03a1e
2 parents cbe8efa + 4ec5d7f
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diff --git a/tutorials/09-variational-inference/index.qmd b/tutorials/09-variational-inference/index.qmd
@@ -332,7 +332,7 @@ test = Matrix(test_cut[:, remove_names]);
 # Bayesian linear regression.
 @model function linear_regression(x, y, n_obs, n_vars, ::Type{T}=Vector{Float64}) where {T}
     # Set variance prior.
-    σ² ~ truncated(Normal(0, 100), 0, Inf)
+    σ² ~ truncated(Normal(0, 100); lower=0)
 
     # Set intercept prior.
     intercept ~ Normal(0, 3)

diff --git a/tutorials/docs-13-using-turing-performance-tips/index.qmd b/tutorials/docs-13-using-turing-performance-tips/index.qmd
@@ -81,7 +81,7 @@ The following example with abstract types
     p, n = size(x)
     params = Vector{Real}(undef, n)
     for i in 1:n
-        params[i] ~ truncated(Normal(), 0, Inf)
+        params[i] ~ truncated(Normal(); lower=0)
     end
 
     a = x * params
@@ -96,7 +96,7 @@ can be transformed into the following representation with concrete types:
     p, n = size(x)
     params = Vector{T}(undef, n)
     for i in 1:n
-        params[i] ~ truncated(Normal(), 0, Inf)
+        params[i] ~ truncated(Normal(); lower=0)
     end
 
     a = x * params
@@ -108,7 +108,7 @@ Alternatively, you could use `filldist` in this example:
 
 ```{julia}
 @model function tmodel(x, y)
-    params ~ filldist(truncated(Normal(), 0, Inf), size(x, 2))
+    params ~ filldist(truncated(Normal(); lower=0), size(x, 2))
     a = x * params
     return y ~ MvNormal(a, I)
 end