From c8c0b27850cd71b9e32d3bf2d7b196c8f2b60409 Mon Sep 17 00:00:00 2001 From: jgabry Date: Tue, 12 Sep 2023 10:55:56 -0600 Subject: [PATCH] update hmm-example.Rmd to new syntax --- knitr/hmm-example/hmm-example.Rmd | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/knitr/hmm-example/hmm-example.Rmd b/knitr/hmm-example/hmm-example.Rmd index 7619d43b2..d2aca4f52 100644 --- a/knitr/hmm-example/hmm-example.Rmd +++ b/knitr/hmm-example/hmm-example.Rmd @@ -38,7 +38,7 @@ of the initial state. Posterior draws from the hidden states can be computed separately. A more complete mathematical definition of the HMM model and function interface -is given in the [Hidden Markov Models](https://mc-stan.org/docs/2_24/functions-reference/hidden-markov-models.html) +is given in the [Hidden Markov Models](https://mc-stan.org/docs/functions-reference/hidden-markov-models.html) section of the Function Reference Guide. There are three functions @@ -120,7 +120,7 @@ The data is the previously generated sequence of $N$ measurements: ```{stan, output.var = "", eval = FALSE} data { int N; // Number of observations - real y[N]; + array[N] real y; } ``` @@ -267,7 +267,7 @@ can be generated with `hmm_latent_rng`: ```{stan, output.var = "", eval = FALSE} generated quantities { - int[N] y_sim = hmm_latent_rng(log_omega, Gamma, rho) + array[N] int y_sim = hmm_latent_rng(log_omega, Gamma, rho); } ```