Fix links to API docs

Author: penelopeysm

tutorials/hidden-markov-models/index.qmd

@@ -123,11 +123,11 @@ The priors on our transition matrix are noninformative, using `T[i] ~ Dirichlet(
 end;
 ```
 
-We will use a combination of two samplers ([HMC](https://turinglang.org/dev/docs/library/#Turing.Inference.HMC) and [Particle Gibbs](https://turinglang.org/dev/docs/library/#Turing.Inference.PG)) by passing them to the [Gibbs](https://turinglang.org/dev/docs/library/#Turing.Inference.Gibbs) sampler. The Gibbs sampler allows for compositional inference, where we can utilize different samplers on different parameters.
+We will use a combination of two samplers (HMC and Particle Gibbs) by passing them to the Gibbs sampler. The Gibbs sampler allows for compositional inference, where we can utilize different samplers on different parameters. (For API details of these samplers, please see [Turing.jl's API documentation](https://turinglang.org/Turing.jl/stable/api/Inference/).)
 
 In this case, we use HMC for `m` and `T`, representing the emission and transition matrices respectively. We use the Particle Gibbs sampler for `s`, the state sequence. You may wonder why it is that we are not assigning `s` to the HMC sampler, and why it is that we need compositional Gibbs sampling at all.
 
-The parameter `s` is not a continuous variable. It is a vector of **integers**, and thus Hamiltonian methods like HMC and [NUTS](https://turinglang.org/dev/docs/library/#Turing.Inference.NUTS) won't work correctly. Gibbs allows us to apply the right tools to the best effect. If you are a particularly advanced user interested in higher performance, you may benefit from setting up your Gibbs sampler to use [different automatic differentiation]({{<meta using-turing-autodiff>}}#compositional-sampling-with-differing-ad-modes) backends for each parameter space.
+The parameter `s` is not a continuous variable. It is a vector of **integers**, and thus Hamiltonian methods like HMC and NUTS won't work correctly. Gibbs allows us to apply the right tools to the best effect. If you are a particularly advanced user interested in higher performance, you may benefit from setting up your Gibbs sampler to use [different automatic differentiation]({{<meta using-turing-autodiff>}}#compositional-sampling-with-differing-ad-modes) backends for each parameter space.
 
 Time to run our sampler.