Fix turing.ml links (#1998)

Author: devmotion

HISTORY.md

@@ -29,7 +29,7 @@
 - Update elliptical slice sampling to use [EllipticalSliceSampling.jl](https://github.com/TuringLang/EllipticalSliceSampling.jl) on the backend. [#1145](https://github.com/TuringLang/Turing.jl/pull/1145). Nothing should change from a front-end perspective -- you can still call `sample(model, ESS(), 1000)`.
 - Added default progress loggers in [#1149](https://github.com/TuringLang/Turing.jl/pull/1149).
 - The symbols used to define the AD backend have changed to be the lowercase form of the package name used for AD. `forward_diff` is now `forwarddiff`, `reverse_diff` is now `tracker`, and `zygote` and `reversediff` are newly supported (see below). `forward_diff` and `reverse_diff` are deprecated and are slated to be removed.
-- Turing now has experimental support for Zygote.jl ([#783](https://github.com/TuringLang/Turing.jl/pull/783)) and ReverseDiff.jl ([#1170](https://github.com/TuringLang/Turing.jl/pull/1170)) AD backends. Both backends are experimental, so please report any bugs you find. Zygote does not allow mutation within your model, so please be aware of this issue. You can enable Zygote with `Turing.setadbackend(:zygote)` and you can enable ReverseDiff with `Turing.setadbackend(:reversediff)`, though to use either you must import the package with `using Zygote` or `using ReverseDiff`. `for` loops are not recommended for ReverseDiff or Zygote -- see [performance tips](https://turing.ml/dev/docs/using-turing/performancetips#special-care-for-tracker-and-zygote) for more information. 
+- Turing now has experimental support for Zygote.jl ([#783](https://github.com/TuringLang/Turing.jl/pull/783)) and ReverseDiff.jl ([#1170](https://github.com/TuringLang/Turing.jl/pull/1170)) AD backends. Both backends are experimental, so please report any bugs you find. Zygote does not allow mutation within your model, so please be aware of this issue. You can enable Zygote with `Turing.setadbackend(:zygote)` and you can enable ReverseDiff with `Turing.setadbackend(:reversediff)`, though to use either you must import the package with `using Zygote` or `using ReverseDiff`. `for` loops are not recommended for ReverseDiff or Zygote -- see [performance tips](https://turinglang.org/dev/docs/using-turing/performancetips#special-care-for-codetrackercode-and-codezygotecode) for more information. 
 - Fix MH indexing bug [#1135](https://github.com/TuringLang/Turing.jl/pull/1135).
 - Fix MH array sampling [#1167](https://github.com/TuringLang/Turing.jl/pull/1167).
 - Fix bug in VI where the bijectors where being inverted incorrectly [#1168](https://github.com/TuringLang/Turing.jl/pull/1168).

docs/README.md

@@ -1 +1 @@
-Turing's documentation in this directory is in markdown format. On how to build the documentation, please refer to the [README](https://github.com/TuringLang/turing.ml) file in [turing.ml](https://github.com/TuringLang/turing.ml). 
+Turing's documentation in this directory is in markdown format. On how to build the documentation, please refer to the [README](https://github.com/TuringLang/turinglang.github.io) file in [turinglang.github.io](https://github.com/TuringLang/turinglang.github.io).

docs/src/for-developers/how_turing_implements_abstractmcmc.md

@@ -4,7 +4,7 @@ title: How Turing implements AbstractMCMC
 
 # How Turing implements AbstractMCMC
 
-Prerequisite: [Interface guide](https://turing.ml/dev/docs/for-developers/interface).
+Prerequisite: [Interface guide](https://turinglang.org/dev/docs/for-developers/interface).
 
 ## Introduction
 
@@ -25,7 +25,7 @@ n_samples = 1000
 chn = sample(mod, alg, n_samples)
 ```
 
-The function `sample` is part of the AbstractMCMC interface. As explained in the [interface guide](https://turing.ml/dev/docs/for-developers/interface), building a a sampling method that can be used by `sample` consists in overloading the structs and functions in `AbstractMCMC`. The interface guide also gives a standalone example of their implementation, [`AdvancedMH.jl`]().
+The function `sample` is part of the AbstractMCMC interface. As explained in the [interface guide](https://turinglang.org/dev/docs/for-developers/interface), building a a sampling method that can be used by `sample` consists in overloading the structs and functions in `AbstractMCMC`. The interface guide also gives a standalone example of their implementation, [`AdvancedMH.jl`]().
 
 Turing sampling methods (most of which are written [here](https://github.com/TuringLang/Turing.jl/tree/master/src/inference)) also implement `AbstractMCMC`. Turing defines a particular architecture for `AbstractMCMC` implementations, that enables working with models defined by the `@model` macro, and uses DynamicPPL as a backend. The goal of this page is to describe this architecture, and how you would go about implementing your own sampling method in Turing, using Importance Sampling as an example. I don't go into all the details: for instance, I don't address selectors or parallelism.
 
@@ -226,7 +226,7 @@ p\left(\theta_{\text{prop}}, x_{\text{obs}}\right)
 
 with ``\theta_{\text{prop}}`` a sample from the proposal and ``x_{\text{obs}}`` the observed data.
 
-This begs the question: how can these functions access model information during sampling? Recall that the model is stored as an instance `m` of `Model`. One of the attributes of `m` is the model evaluation function `m.f`, which is built by compiling the `@model` macro. Executing `f` runs the tilde statements of the model in order, and adds model information to the sampler (the instance of `Sampler` that stores information about the ongoing sampling process) at each step (see [here](https://turing.ml/dev/docs/for-developers/compiler) for more information about how the `@model` macro is compiled). The DynamicPPL functions `assume` and `observe` determine what kind of information to add to the sampler for every tilde statement.
+This begs the question: how can these functions access model information during sampling? Recall that the model is stored as an instance `m` of `Model`. One of the attributes of `m` is the model evaluation function `m.f`, which is built by compiling the `@model` macro. Executing `f` runs the tilde statements of the model in order, and adds model information to the sampler (the instance of `Sampler` that stores information about the ongoing sampling process) at each step (see [here](https://turinglang.org/dev/docs/for-developers/compiler) for more information about how the `@model` macro is compiled). The DynamicPPL functions `assume` and `observe` determine what kind of information to add to the sampler for every tilde statement.
 
 Consider an instance `m` of `Model` and a sampler `spl`, with associated `VarInfo` `vi = spl.state.vi`. At some point during the sampling process, an AbstractMCMC function such as `step!` calls  `m(vi, ...)`, which calls the model evaluation function `m.f(vi, ...)`.