Fix CDF and CCDF definitions

src/functions-reference/conventions_for_probability_functions.Rmd

@@ -41,7 +41,7 @@ Each probability function has a specific outcome value and a number of
 parameters.  Following conditional probability notation, probability
 density and mass functions use a vertical bar to separate the outcome
 from the parameters of the distribution.  For example, `normal_lpdf(y
-| mu, sigma)` returns the value of mathematical formula $log
+| mu, sigma)` returns the value of mathematical formula $\log
 \text{Normal}(y \, | \, \mu, \sigma)$. Cumulative distribution
 functions separate the outcome from the parameters in the same way
 (e.g., `normal_lcdf(y_low | mu, sigma)`
@@ -116,9 +116,9 @@ function.
 
 For a univariate random variable $Y$ with probability function $p_Y(y
 \, | \, \theta)$, the cumulative distribution function (CDF) $F_Y$ is
-defined by \[ F_Y(y) \ = \ \text{Pr}[Y < y] \ = \ \int_{-\infty}^y p(y
+defined by \[ F_Y(y) \ = \ \text{Pr}[Y \le y] \ = \ \int_{-\infty}^y p(y
 \, | \, \theta) \ \text{d}y. \] The complementary cumulative
-distribution function (CCDF) is defined as \[ \text{Pr}[Y \geq y] \ =
+distribution function (CCDF) is defined as \[ \text{Pr}[Y > y] \ =
 \ 1 - F_Y(y). \] The reason to use CCDFs instead of CDFs in
 floating-point arithmetic is that it is possible to represent numbers
 very close to 0 (the closest you can get is roughly $10^{-300}$), but
@@ -282,4 +282,3 @@ arguments, as all non-scalar arguments must have matching size.
 Discrete distributions return `ints` and continuous distributions
 return `reals`, each of appropriate size.  The symbol `R` denotes such
 a return type.
-