Add a specification for Discrete Distribution

The description how to imitate the discrete distribution with histogram
is replaced by the specification for the discrete distribution.

The discrete distribution inherits its grammar from the histogram.
The only difference is that there's no need for starting b_0 boundary,
and boundaries are worded as discrete values.

Closes #29

Author: rakhimov

mef/schema/random_deviate.rnc

@@ -11,4 +11,6 @@ beta-deviate = element beta-deviate { expression, expression }
 
 histogram = element histogram { expression, bin+ }
 
+discrete = element discrete { bin+ }
+
 bin = element bin { expression, expression }

mef/schema/stochastic_layer.rnc

@@ -18,5 +18,6 @@ random-deviate =
   | gamma-deviate
   | beta-deviate
   | histogram
+  | discrete
 
 test-event = test-initiating-event | test-functional-event

mef/stochastic_layer.rst

@@ -533,7 +533,7 @@ When used to perform Monte-Carlo simulations,
 they return a number drawn at pseudo-random according to their type.
 The Model Exchange Format includes two types of random deviates:
 built-in deviates like uniform, normal or lognormal,
-and histograms that are user defined discrete distributions.
+and user defined discrete distributions like histograms.
 A preliminary list of distributions is summarized in :numref:`table_random_deviates`.
 As for arithmetic operators and built-ins, this list can be extended on demand.
 
@@ -556,6 +556,8 @@ As for arithmetic operators and built-ins, this list can be extended on demand.
     +-----------------------+------------+-------------------------------------------------------------------------------------------------------------+
     | **histograms**        | any        | discrete distributions defined by means of a list of pairs                                                  |
     +-----------------------+------------+-------------------------------------------------------------------------------------------------------------+
+    | **discrete**          | >1         | discrete distributions defined by means of a list of pairs                                                  |
+    +-----------------------+------------+-------------------------------------------------------------------------------------------------------------+
 
 Uniform Deviates
     These primitives describe uniform distributions in a given range
@@ -685,9 +687,7 @@ Histograms
 
         \mathbf{E}(X) = \frac{1}{x_n - x_0} \times \sum_{i=1}^{n}(x_i - x_{i-1})\mathbf{E}(E_i)
 
-    Both Cumulative Distribution Functions
-    and Density Probability Distributions can be translated into histograms.
-
+    Cumulative Distribution Functions can be translated into histograms.
     A Cumulative Distribution Function is a list of pairs
     :math:`(p_1, v_1), \ldots, (p_n, v_n)`,
     where the :math:`p_i`'s are
@@ -701,17 +701,31 @@ Histograms
     with the initial value
     :math:`x_0 = 0, x_1 = p_1, \text{ and } x_i = p_i - p_{i-1} \text{ for all } i>1`.
 
-    A Discrete Probability Distribution is a list of pairs
-    :math:`(d_1, m_1), \ldots, (d_n, m_n)`.
-    The :math:`d_i`'s are probability densities.
-    However, they could be any kind of values.
-    The :math:`m_i`'s are midpoints of intervals
-    and are such that :math:`m_1 < m_2 < \ldots < m_n < 1`.
-    The histogram that corresponds to a Discrete Probability Distribution
-    :math:`(d_1, m_1), \ldots, (d_n, m_n)`
-    is the list of pairs :math:`(x_1, d_1), \ldots, (x_n, d_n)`,
-    with the initial value
-    :math:`x_0 = 0, x_1 = 2m_1, \text{ and } x_i = x_{i-1} + 2(m_i - x_{i-1})`.
+Discrete Distribution
+    A discrete distribution is a statistical distribution
+    whose variables can only take discrete values.
+    It is defined by a list of pairs
+    :math:`(a_1, w_1), \ldots, (a_n, w_n)`.
+    The :math:`a_i`'s are the values of the distribution,
+    and the :math:`w_i` are their respective non-negative weights.
+
+    The probability density function of the discrete distribution:
+
+    .. math::
+
+        f(x;w_1,\ldots,w_n) = \frac{w_k}{\sum_{i=1}^{n}w_i}
+
+    Where :math:`k` is such that
+
+    .. math::
+
+        x = a_k \quad \forall k \in \mathbb{Z} : 1 \leq k \leq n
+
+    The default value is the expected value of the distribution.
+
+    .. math::
+
+        E(x) = \dfrac{\sum_{i=1}^{n}a_i \cdot w_i}{\sum_{i=1}^{n}w_i}
 
 
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