Add distribution statements

Author: WardBrian

src/functions-reference/embedded_laplace.qmd

@@ -219,6 +219,22 @@ The arguments required to compute this likelihood are:
 * `y_index`: an array whose $i^\text{th}$ element indicates to which
 group the $i^\text{th}$ observation belongs to.
 
+<!-- real; laplace_marginal_poisson_log ~; -->
+\index{{\tt \bfseries laplace\_marginal\_poisson\_log }!sampling statement|hyperpage}
+
+`y ~ ` **`laplace_marginal_poisson_log`**`(y_index, theta0, K_function, (...))`<br>\newline
+
+Increment target log probability density with `laplace_marginal_poisson_log_lupmf(y | y_index, theta0, K_function, (...))`.
+{{< since 2.37 >}}
+
+<!-- real; laplace_marginal_tol_poisson_log ~; -->
+\index{{\tt \bfseries laplace\_marginal\_tol\_poisson\_log }!sampling statement|hyperpage}
+
+`y ~ ` **`laplace_marginal_tol_poisson_log`**`(y_index, theta0, K_function, (...), tol, max_steps, hessian_block_size, solver, max_steps_linesearch)`<br>\newline
+
+Increment target log probability density with `laplace_marginal_tol_poisson_log_lupmf(y | y_index, theta0, K_function, (...), tol, max_steps, hessian_block_size, solver, max_steps_linesearch)`.
+{{< since 2.37 >}}
+
 The signatures for the embedded Laplace approximation function with a Poisson
 likelihood are
 
@@ -259,6 +275,24 @@ to the rate parameter of the Poisson. The likelihood is then,
 $$
 p(y \mid \theta, \phi) = \prod_i\text{Poisson} (y_i \mid \exp(\theta_{g(i)}) x_i).
 $$
+
+<!-- real; laplace_marginal_poisson_2_log ~; -->
+\index{{\tt \bfseries laplace\_marginal\_poisson\_2\_log }!sampling statement|hyperpage}
+
+`y ~ ` **`laplace_marginal_poisson_2_log`**`(y_index, x, theta0, K_function, (...))`<br>\newline
+
+Increment target log probability density with `laplace_marginal_poisson_2_log_lupmf(y | y_index, x, theta0, K_function, (...))`.
+{{< since 2.37 >}}
+
+<!-- real; laplace_marginal_tol_poisson_2_log ~; -->
+\index{{\tt \bfseries laplace\_marginal\_tol\_poisson\_2\_log }!sampling statement|hyperpage}
+
+`y ~ ` **`laplace_marginal_tol_poisson_2_log`**`(y_index, x, theta0, K_function, (...), tol, max_steps, hessian_block_size, solver, max_steps_linesearch)`<br>\newline
+
+Increment target log probability density with `laplace_marginal_tol_poisson_2_log_lupmf(y | y_index, x, theta0, K_function, (...), tol, max_steps, hessian_block_size, solver, max_steps_linesearch)`.
+{{< since 2.37 >}}
+
+
 The signatures for this function are:
 
 <!-- real; laplace_marginal_poisson_2_log_lpmf; (array[] int y | array[] int y_index, vector x, vector theta0, function K_function, tuple(...)); -->
@@ -313,6 +347,23 @@ The arguments for the likelihood function are:
 group the $i^\text{th}$ observation belongs to.
 * `eta`: the overdispersion parameter.
 
+<!-- real; laplace_marginal_neg_binomial_2_log ~; -->
+\index{{\tt \bfseries laplace\_marginal\_neg\_binomial\_2\_log }!sampling statement|hyperpage}
+
+`y ~ ` **`laplace_marginal_neg_binomial_2_log`**`(y_index, eta, theta0, K_function, (...))`<br>\newline
+
+Increment target log probability density with `laplace_marginal_neg_binomial_2_log_lupmf(y | y_index, eta, theta0, K_function, (...))`.
+{{< since 2.37 >}}
+
+<!-- real; laplace_marginal_tol_neg_binomial_2_log ~; -->
+\index{{\tt \bfseries laplace\_marginal\_tol\_neg\_binomial\_2\_log }!sampling statement|hyperpage}
+
+`y ~ ` **`laplace_marginal_tol_neg_binomial_2_log`**`(y_index, eta, theta0, K_function, (...), tol, max_steps, hessian_block_size, solver, max_steps_linesearch)`<br>\newline
+
+Increment target log probability density with `laplace_marginal_tol_neg_binomial_2_log_lupmf(y | y_index, eta, theta0, K_function, (...), tol, max_steps, hessian_block_size, solver, max_steps_linesearch)`.
+{{< since 2.37 >}}
+
+
 The function signatures for the embedded Laplace approximation with a negative
 Binomial likelihood are
 <!-- real; laplace_marginal_neg_binomial_2_log_lpmf; (array[] int y | array[] int y_index, real eta, vector theta0, function K_function, tuple(...)); -->
@@ -359,6 +410,23 @@ The arguments of the likelihood function are:
 * `y_index`: an array whose $i^\text{th}$ element indicates to which
 group the $i^\text{th}$ observation belongs to.
 
+<!-- real; laplace_marginal_bernoulli_logit ~; -->
+\index{{\tt \bfseries laplace\_marginal\_bernoulli\_logit }!sampling statement|hyperpage}
+
+`y ~ ` **`laplace_marginal_bernoulli_logit`**`(y_index, theta0, K_function, (...))`<br>\newline
+
+Increment target log probability density with `laplace_marginal_bernoulli_logit_lupmf(y | y_index, theta0, K_function, (...))`.
+{{< since 2.37 >}}
+
+<!-- real; laplace_marginal_tol_bernoulli_logit ~; -->
+\index{{\tt \bfseries laplace\_marginal\_tol\_bernoulli\_logit }!sampling statement|hyperpage}
+
+`y ~ ` **`laplace_marginal_tol_bernoulli_logit`**`(y_index, theta0, K_function, (...), tol, max_steps, hessian_block_size, solver, max_steps_linesearch)`<br>\newline
+
+Increment target log probability density with `laplace_marginal_tol_bernoulli_logit_lupmf(y | y_index, theta0, K_function, (...), tol, max_steps, hessian_block_size, solver, max_steps_linesearch)`.
+{{< since 2.37 >}}
+
+
 The function signatures for the embedded Laplace approximation with a Bernoulli likelihood are
 
 <!-- real; laplace_marginal_bernoulli_logit_lpmf; (array[] int y | array[] int y_index, vector theta0, function K_function, tuple(...)); -->