Add troubleshooting page

usage/troubleshooting/index.qmd

@@ -16,9 +16,10 @@ If the suggestions here do not resolve your problem, please do feel free to [ope
 
 ```{julia}
 using Turing
+Turing.setprogress!(false)
 ```
 
-## T0001
+## Initial parameters
 
 > failed to find valid initial parameters in {N} tries. This may indicate an error with the model or AD backend...
 
@@ -35,12 +36,12 @@ Here is an example with a model that is known to be problematic:
 using Turing
 using DynamicPPL.TestUtils.AD: run_ad
 
-@model function t0001_bad()
+@model function initial_bad()
     a ~ Normal()
     x ~ truncated(Normal(a), 0, Inf)
 end
 
-model = t0001_bad()
+model = initial_bad()
 adtype = AutoForwardDiff()
 result = run_ad(model, adtype; test=false, benchmark=false)
 result.grad_actual
@@ -53,12 +54,12 @@ In this case, the `NaN` gradient is caused by the `Inf` argument to `truncated`.
 Here, the upper bound of `Inf` is not needed, so it can be removed:
 
 ```{julia}
-@model function t0001_good()
+@model function initial_good()
     a ~ Normal()
     x ~ truncated(Normal(a); lower=0)
 end
 
-model = t0001_good()
+model = initial_good()
 adtype = AutoForwardDiff()
 run_ad(model, adtype; test=false, benchmark=false).grad_actual
 ```
@@ -71,11 +72,19 @@ Another cause of this error is having models with very extreme parameters.
 This example is taken from [this Turing.jl issue](https://github.com/TuringLang/Turing.jl/issues/2476):
 
 ```{julia}
-@model function t0001_bad2()
+@model function initial_bad2()
 	  x ~ Exponential(100)
 	  y ~ Uniform(0, x)
 end
-model = t0001_bad2() | (y = 50.0,)
+model = initial_bad2() | (y = 50.0,)
+
+@model function initial_bad3()
+    x_trf ~ Uniform(0, 1)
+    x := -log(x_trf) / 100
+    @show x
+	  y ~ Uniform(0, x)
+end
+model3 = initial_bad3() | (y = 50.0,)
 ```
 
 The problem here is that HMC attempts to find initial values for parameters inside the region of `[-2, 2]`, _after_ the parameters have been transformed to unconstrained space.
@@ -94,5 +103,10 @@ logjoint(model, (x = exp(2),))
 ```
 
 The most direct way of fixing this is to manually provide a set of initial parameters that are valid.
-For example, you can obtain a set of initial parameters with `rand(Dict, model)`, and then pass this as the `initial_params` keyword argument to `sample`.
-Otherwise, though, you may want to consider reparameterising the model to avoid such issues.
+For example, you can obtain a set of initial parameters with `rand(Vector, model)`, and then pass this as the `initial_params` keyword argument to `sample`:
+
+```{julia}
+sample(model, NUTS(), 1000; initial_params=rand(Vector, model))
+```
+
+More generally, you may also consider reparameterising the model to avoid such issues.