Merge pull request #123 from stan-dev/bugfix/121-non-negative-distributions

Move content of the non-negative distributions chapter to other chapters

Author: Bob Carpenter

src/functions-reference/_bookdown.yml

@@ -22,11 +22,10 @@ rmd_files: [
   "continuous_distributions.Rmd",
   "unbounded_continuous_distributions.Rmd",
   "positive_continuous_distributions.Rmd",
-  "non-negative_continuous_distributions.Rmd",
-  "positive_lower-bounded_probabilities.Rmd",
+  "positive_lower-bounded_distributions.Rmd",
   "continuous_distributions_on_[0_1].Rmd",
   "circular_distributions.Rmd",
-  "bounded_continuous_probabilities.Rmd",
+  "bounded_continuous_distributions.Rmd",
   "distributions_over_unbounded_vectors.Rmd",
   "simplex_distributions.Rmd",
   "correlation_matrix_distributions.Rmd",

src/functions-reference/binary_distributions.Rmd

@@ -24,7 +24,7 @@ If $\theta \in [0,1]$, then for $y \in \{0,1\}$, \[
 
 `y ~ ` **`bernoulli`**`(theta)`
 
-Increment target log probability density with `bernoulli_lpmf( y | theta)`
+Increment target log probability density with `bernoulli_lpmf(y | theta)`
 dropping constant additive terms.
 <!-- real; bernoulli; ~; -->
 \index{{\tt \bfseries bernoulli }!sampling statement|hyperpage}
@@ -87,7 +87,7 @@ If $\alpha \in \mathbb{R}$, then for $y \in \{0,1\}$, \[
 
 `y ~ ` **`bernoulli_logit`**`(alpha)`
 
-Increment target log probability density with `bernoulli_logit_lpmf( y | alpha)`
+Increment target log probability density with `bernoulli_logit_lpmf(y | alpha)`
 dropping constant additive terms.
 <!-- real; bernoulli_logit; ~; -->
 \index{{\tt \bfseries bernoulli\_logit }!sampling statement|hyperpage}
@@ -135,7 +135,7 @@ n}\text{Bernoulli}(y_i~|~\text{logit}^{-1}(\alpha_i + x_i\cdot
 
 `y ~ ` **`bernoulli_logit_glm`**`(x, alpha, beta)`
 
-Increment target log probability density with `bernoulli_logit_glm_lpmf( y | x, alpha, beta)`
+Increment target log probability density with `bernoulli_logit_glm_lpmf(y | x, alpha, beta)`
 dropping constant additive terms.
 <!-- real; bernoulli_logit_glm; ~; -->
 \index{{\tt \bfseries bernoulli\_logit\_glm }!sampling statement|hyperpage}

…nce/bounded_continuous_probabilities.Rmd …nce/bounded_continuous_distributions.Rmdsrc/functions-reference/bounded_continuous_probabilities.Rmd renamed to src/functions-reference/bounded_continuous_distributions.Rmd src/functions-reference/bounded_continuous_probabilities.Rmd renamed to src/functions-reference/bounded_continuous_distributions.Rmd

@@ -1,4 +1,4 @@
-# Bounded Continuous Probabilities
+# Bounded Continuous Distributions
 
 The bounded continuous probabilities have support on a finite interval
 of real numbers.
@@ -21,7 +21,7 @@ $y \in [\alpha,\beta]$, \[ \text{Uniform}(y|\alpha,\beta) =
 
 `y ~ ` **`uniform`**`(alpha, beta)`
 
-Increment target log probability density with `uniform_lpdf( y | alpha, beta)`
+Increment target log probability density with `uniform_lpdf(y | alpha, beta)`
 dropping constant additive terms.
 <!-- real; uniform ~; -->
 \index{{\tt \bfseries uniform }!sampling statement|hyperpage}

src/functions-reference/bounded_discrete_distributions.Rmd

@@ -38,7 +38,7 @@ Suppose $N \in \mathbb{N}$ and $\theta \in [0,1]$, and $n \in
 
 `n ~ ` **`binomial`**`(N, theta)`
 
-Increment target log probability density with `binomial_lpmf( n | N, theta)`
+Increment target log probability density with `binomial_lpmf(n | N, theta)`
 dropping constant additive terms.
 <!-- real ; binomial ~; -->
 \index{{\tt \bfseries binomial }!sampling statement|hyperpage}
@@ -115,7 +115,7 @@ n}{\text{logit}^{-1}(\alpha)} \]
 
 `n ~ ` **`binomial_logit`**`(N, alpha)`
 
-Increment target log probability density with `binomial_logit_lpmf( n | N, alpha)`
+Increment target log probability density with `binomial_logit_lpmf(n | N, alpha)`
 dropping constant additive terms.
 <!-- real; binomial_logit ~; -->
 \index{{\tt \bfseries binomial\_logit }!sampling statement|hyperpage}
@@ -145,7 +145,7 @@ If $N \in \mathbb{N}$, $\alpha \in \mathbb{R}^+$, and $\beta \in
 
 `n ~ ` **`beta_binomial`**`(N, alpha, beta)`
 
-Increment target log probability density with `beta_binomial_lpmf( n | N, alpha, beta)`
+Increment target log probability density with `beta_binomial_lpmf(n | N, alpha, beta)`
 dropping constant additive terms.
 <!-- real; beta_binomial ~; -->
 \index{{\tt \bfseries beta\_binomial }!sampling statement|hyperpage}
@@ -207,7 +207,7 @@ If $a \in \mathbb{N}$, $b \in \mathbb{N}$, and $N \in
 
 `n ~ ` **`hypergeometric`**`(N, a, b)`
 
-Increment target log probability density with `hypergeometric_lpmf( n | N, a, b)`
+Increment target log probability density with `hypergeometric_lpmf(n | N, a, b)`
 dropping constant additive terms.
 <!-- real; hypergeometric ~; -->
 \index{{\tt \bfseries hypergeometric }!sampling statement|hyperpage}
@@ -245,7 +245,7 @@ distribution, \[ \text{CategoricalLogit}(y~|~\beta) =
 
 `y ~ ` **`categorical`**`(theta)`
 
-Increment target log probability density with `categorical_lpmf( y | theta)`
+Increment target log probability density with `categorical_lpmf(y | theta)`
 dropping constant additive terms.
 <!-- real; categorical ~; -->
 \index{{\tt \bfseries categorical }!sampling statement|hyperpage}
@@ -254,7 +254,7 @@ dropping constant additive terms.
 
 `y ~ ` **`categorical_logit`**`(beta)`
 
-Increment target log probability density with `categorical_logit_lpmf( y | beta)`
+Increment target log probability density with `categorical_logit_lpmf(y | beta)`
 dropping constant additive terms.
 <!-- real; categorical_logit ~; -->
 \index{{\tt \bfseries categorical\_logit }!sampling statement|hyperpage}
@@ -318,7 +318,7 @@ $\text{logit}^{-1}(\infty) = 1$.
 
 `k ~ ` **`ordered_logistic`**`(eta, c)`
 
-Increment target log probability density with `ordered_logistic_lpmf( k | eta, c)`
+Increment target log probability density with `ordered_logistic_lpmf(k | eta, c)`
 dropping constant additive terms.
 <!-- real; ordered_logistic ~; -->
 \index{{\tt \bfseries ordered\_logistic }!sampling statement|hyperpage}
@@ -360,7 +360,7 @@ $\Phi(\infty) = 1$.
 
 `k ~ ` **`ordered_probit`**`(eta, c)`
 
-Increment target log probability density with `ordered_probit_lpmf( k | eta, c)`
+Increment target log probability density with `ordered_probit_lpmf(k | eta, c)`
 dropping constant additive terms.
 <!-- real; ordered_probit ~; -->
 \index{{\tt \bfseries ordered\_probit }!sampling statement|hyperpage}

src/functions-reference/circular_distributions.Rmd

@@ -39,7 +39,7 @@ of the values of $y$ or $\mu$).
 
 `y ~ ` **`von_mises`**`(mu, kappa)`
 
-Increment target log probability density with `von_mises_lpdf( y | mu, kappa)`
+Increment target log probability density with `von_mises_lpdf(y | mu, kappa)`
 dropping constant additive terms.
 <!-- real; von_mises ~; -->
 \index{{\tt \bfseries von\_mises }!sampling statement|hyperpage}

src/functions-reference/continuous_distributions_on_[0_1].Rmd

@@ -29,7 +29,7 @@ distribution requires strictly positive parameters, $\alpha, \beta >
 
 `theta ~ ` **`beta`**`(alpha, beta)`
 
-Increment target log probability density with `beta_lpdf( theta | alpha, beta)`
+Increment target log probability density with `beta_lpdf(theta | alpha, beta)`
 dropping constant additive terms.
 <!-- real; beta ~; -->
 \index{{\tt \bfseries beta }!sampling statement|hyperpage}
@@ -96,7 +96,7 @@ parameter, $\kappa > 0$.
 
 `theta ~ ` **`beta_proportion`**`(mu, kappa)`
 
-Increment target log probability density with `beta_proportion_lpdf( theta | mu, kappa)`
+Increment target log probability density with `beta_proportion_lpdf(theta | mu, kappa)`
 dropping constant additive terms.
 <!-- real; beta_proportion ~; -->
 \index{{\tt \bfseries beta\_proportion }!sampling statement|hyperpage}

src/functions-reference/correlation_matrix_distributions.Rmd

@@ -59,7 +59,7 @@ practice.
 
 `y ~ ` **`lkj_corr`**`(eta)`
 
-Increment target log probability density with `lkj_corr_lpdf( y | eta)`
+Increment target log probability density with `lkj_corr_lpdf(y | eta)`
 dropping constant additive terms.
 <!-- real; lkj_corr ~; -->
 \index{{\tt \bfseries lkj\_corr }!sampling statement|hyperpage}
@@ -127,7 +127,7 @@ unit Euclidean length.
 
 `L ~ ` **`lkj_corr_cholesky`**`(eta)`
 
-Increment target log probability density with `lkj_corr_cholesky_lpdf( L | eta)`
+Increment target log probability density with `lkj_corr_cholesky_lpdf(L | eta)`
 dropping constant additive terms.
 <!-- real; lkj_corr_cholesky ~; -->
 \index{{\tt \bfseries lkj\_corr\_cholesky }!sampling statement|hyperpage}

src/functions-reference/covariance_matrix_distributions.Rmd

@@ -29,7 +29,7 @@ Gamma function, \[ \Gamma_K(x) = \frac{1}{\pi^{K(K-1)/4}} \
 
 `W ~ ` **`wishart`**`(nu, Sigma)`
 
-Increment target log probability density with `wishart_lpdf( W | nu, Sigma)`
+Increment target log probability density with `wishart_lpdf(W | nu, Sigma)`
 dropping constant additive terms.
 <!-- real; wishart ~; -->
 \index{{\tt \bfseries wishart }!sampling statement|hyperpage}
@@ -68,7 +68,7 @@ for symmetric and positive-definite $W \in \mathbb{R}^{K \times K}$,
 
 `W ~ ` **`inv_wishart`**`(nu, Sigma)`
 
-Increment target log probability density with `inv_wishart_lpdf( W | nu, Sigma)`
+Increment target log probability density with `inv_wishart_lpdf(W | nu, Sigma)`
 dropping constant additive terms.
 <!-- real; inv_wishart ~; -->
 \index{{\tt \bfseries inv\_wishart }!sampling statement|hyperpage}

src/functions-reference/distributions_over_unbounded_vectors.Rmd

@@ -31,7 +31,7 @@ $\Sigma$.
 
 `y ~ ` **`multi_normal`**`(mu, Sigma)`
 
-Increment target log probability density with `multi_normal_lpdf( y | mu, Sigma)`
+Increment target log probability density with `multi_normal_lpdf(y | mu, Sigma)`
 dropping constant additive terms.
 <!-- real; multi_normal ~; -->
 \index{{\tt \bfseries multi\_normal }!sampling statement|hyperpage}
@@ -121,7 +121,7 @@ for $y \in \mathbb{R}^K$, \[ \text{MultiNormalPrecision}(y|\mu,\Omega)
 
 `y ~ ` **`multi_normal_prec`**`(mu, Omega)`
 
-Increment target log probability density with `multi_normal_prec_lpdf( y | mu, Omega)`
+Increment target log probability density with `multi_normal_prec_lpdf(y | mu, Omega)`
 dropping constant additive terms.
 <!-- real; multi_normal_prec ~; -->
 \index{{\tt \bfseries multi\_normal\_prec }!sampling statement|hyperpage}
@@ -174,7 +174,7 @@ probability functions will raise errors.
 
 `y ~ ` **`multi_normal_cholesky`**`(mu, L)`
 
-Increment target log probability density with `multi_normal_cholesky_lpdf( y | mu, L)`
+Increment target log probability density with `multi_normal_cholesky_lpdf(y | mu, L)`
 dropping constant additive terms.
 <!-- real; multi_normal_cholesky ~; -->
 \index{{\tt \bfseries multi\_normal\_cholesky }!sampling statement|hyperpage}
@@ -263,7 +263,7 @@ function does not take into account the mean prediction.
 
 `y ~ ` **`multi_gp`**`(Sigma, w)`
 
-Increment target log probability density with `multi_gp_lpdf( y | Sigma, w)`
+Increment target log probability density with `multi_gp_lpdf(y | Sigma, w)`
 dropping constant additive terms.
 <!-- real; multi_gp ~; -->
 \index{{\tt \bfseries multi\_gp }!sampling statement|hyperpage}
@@ -299,7 +299,7 @@ account the mean prediction.
 
 `y ~ ` **`multi_gp_cholesky`**`(L, w)`
 
-Increment target log probability density with `multi_gp_cholesky_lpdf( y | L, w)`
+Increment target log probability density with `multi_gp_cholesky_lpdf(y | L, w)`
 dropping constant additive terms.
 <!-- real; multi_gp_cholesky ~; -->
 \index{{\tt \bfseries multi\_gp\_cholesky }!sampling statement|hyperpage}
@@ -332,7 +332,7 @@ K)/2\right)}      {\Gamma(\nu/2)} \ \frac{1}{\sqrt{\left| \Sigma
 
 `y ~ ` **`multi_student_t`**`(nu, mu, Sigma)`
 
-Increment target log probability density with `multi_student_t_lpdf( y | nu, mu, Sigma)`
+Increment target log probability density with `multi_student_t_lpdf(y | nu, mu, Sigma)`
 dropping constant additive terms.
 <!-- real; multi_student_t ~; -->
 \index{{\tt \bfseries multi\_student\_t }!sampling statement|hyperpage}
@@ -420,7 +420,7 @@ processes observations and avoids a matrix inversions can be used
 
 `y ~ ` **`gaussian_dlm_obs`**`(F, G, V, W, m0, C0)`
 
-Increment target log probability density with `gaussian_dlm_obs_lpdf( y | F, G, V, W, m0, C0)`
+Increment target log probability density with `gaussian_dlm_obs_lpdf(y | F, G, V, W, m0, C0)`
 dropping constant additive terms.
 <!-- real; gaussian_dlm_obs ~; -->
 \index{{\tt \bfseries gaussian\_dlm\_obs }!sampling statement|hyperpage}

src/functions-reference/multivariate_discrete_distributions.Rmd

@@ -24,7 +24,7 @@ multinomial coefficient is defined by \[ \binom{N}{y_1,\ldots,y_k} =
 
 `y ~ ` **`multinomial`**`(theta)`
 
-Increment target log probability density with `multinomial_lpmf( y | theta)`
+Increment target log probability density with `multinomial_lpmf(y | theta)`
 dropping constant additive terms.
 <!-- real; multinomial ~; -->
 \index{{\tt \bfseries multinomial }!sampling statement|hyperpage}