From 8375b20d80fb3598462567c1a10caefa653edb93 Mon Sep 17 00:00:00 2001 From: Shravan Goswami Date: Mon, 29 Sep 2025 09:29:43 +0530 Subject: [PATCH] fix urls --- developers/inference/abstractmcmc-turing/index.qmd | 2 +- faq/index.qmd | 2 +- tutorials/bayesian-logistic-regression/index.qmd | 2 +- tutorials/bayesian-poisson-regression/index.qmd | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/developers/inference/abstractmcmc-turing/index.qmd b/developers/inference/abstractmcmc-turing/index.qmd index 394668344..42c1fd308 100755 --- a/developers/inference/abstractmcmc-turing/index.qmd +++ b/developers/inference/abstractmcmc-turing/index.qmd @@ -262,7 +262,7 @@ $$ 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://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. +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]({{}}) 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, ...)`. diff --git a/faq/index.qmd b/faq/index.qmd index 44fec2659..8519afae9 100644 --- a/faq/index.qmd +++ b/faq/index.qmd @@ -55,7 +55,7 @@ Turing.jl fully supports sampling multiple chains in parallel: - **Multithreaded sampling**: Use `MCMCThreads()` to run one chain per thread - **Distributed sampling**: Use `MCMCDistributed()` for distributed computing -See the [Core Functionality guide]({{< meta core-functionality >}}/#sampling-multiple-chains) for examples. +See the [Core Functionality guide]({{}}#sampling-multiple-chains) for examples. ### 2. Threading Within Models Using threads inside your model (e.g., `Threads.@threads`) requires more care: diff --git a/tutorials/bayesian-logistic-regression/index.qmd b/tutorials/bayesian-logistic-regression/index.qmd index bfa4e68fc..0bdf325ae 100755 --- a/tutorials/bayesian-logistic-regression/index.qmd +++ b/tutorials/bayesian-logistic-regression/index.qmd @@ -152,7 +152,7 @@ chain ::: {.callout-warning collapse="true"} ## Sampling With Multiple Threads The `sample()` call above assumes that you have at least `nchains` threads available in your Julia instance. If you do not, the multiple chains -will run sequentially, and you may notice a warning. For more information, see [the Turing documentation on sampling multiple chains.](https://turinglang.org/dev/docs/using-turing/guide/#sampling-multiple-chains) +will run sequentially, and you may notice a warning. For more information, see [the Turing documentation on sampling multiple chains.]({{}}#sampling-multiple-chains) ::: ```{julia} diff --git a/tutorials/bayesian-poisson-regression/index.qmd b/tutorials/bayesian-poisson-regression/index.qmd index e8e51cb1a..dbbd5fb87 100755 --- a/tutorials/bayesian-poisson-regression/index.qmd +++ b/tutorials/bayesian-poisson-regression/index.qmd @@ -172,7 +172,7 @@ chain ::: {.callout-warning collapse="true"} ## Sampling With Multiple Threads The `sample()` call above assumes that you have at least `nchains` threads available in your Julia instance. If you do not, the multiple chains -will run sequentially, and you may notice a warning. For more information, see [the Turing documentation on sampling multiple chains.](https://turinglang.org/dev/docs/using-turing/guide/#sampling-multiple-chains) +will run sequentially, and you may notice a warning. For more information, see [the Turing documentation on sampling multiple chains.]({{}}#sampling-multiple-chains) ::: # Viewing the Diagnostics