Mention statistical independence of members of set of generators

Also refer to examples/ folder for example of Parallel Monte-Carlo
simulation.

Author: oleksandr-pavlyk

docs/source/reference/mt2203.rst

@@ -1,11 +1,11 @@
 MT2203 brng
 ===========
 
-Each generator from the set of 6024 Mersenne Twister pseudorandom number generators can be 
-initialized with  either an integral seed, a list of integral seeds, or automatically.
+Each generator from the set of `6024 Mersenne Twister pseudorandom number generators <philoxrng_>`_ can be 
+initialized with either an integral seed, a list of integral seeds, or automatically.
 
 An individual member of the set can be addressed by using a tuple to specify the generator 
-:code:`brng=("MT2203", set_id)` where :math:`0 \leq \text{set_id} \le 6024`.
+:code:`brng=("MT2203", set_id)` where :math:`0 \leq \text{set_id} < 6024`.
 
 .. code-block:: python
     :caption: Construction for MT2203 basic random number generator with scalar seed
@@ -44,3 +44,10 @@ When seed is not specified, the generator is initialized using system clock, e.g
         # Use random state instance to generate 1000 random numbers
         # from discrete uniform distribution [1, 6]
         isample = rs_def.randint(1, 6 + 1, size=1000)
+
+Different members of the set of generators initialized with the same seed are designed to generate
+statistically independent streams of randomness. This property makes MT2203 generator suitable for 
+parallelizing stochastic algorithms. Please refer to "examples/" folder in the `GitHub repo
+<https://github.com/IntelPython/mkl_random>`_.
+
+.. _philoxrng: https://spec.oneapi.io/versions/1.0-rev-2/elements/oneMKL/source/domains/rng/mkl-rng-philox4x32x10.html

docs/source/reference/wichmann_hill.rst

@@ -2,11 +2,11 @@ Wichmann-Hill brng
 ==================
 
 Each generator from the set of 273 Wichmann-Hill’s combined multiplicative congruential 
-generators can be initialized with  either an integral seed, a list of integral seeds, 
+`generators <whrng_>`_ can be initialized with  either an integral seed, a list of integral seeds, 
 or automatically.
 
 An individual member of the set can be addressed by using a tuple to specify the generator as
-:code:`brng=("WH", set_id)` where :math:`0 \leq \text{set_id} \le 273`.
+:code:`brng=("WH", set_id)` where :math:`0 \leq \text{set_id} < 273`.
 
 .. code-block:: python
     :caption: Construction for WH basic random number generator with scalar seed
@@ -45,3 +45,10 @@ When seed is not specified, the generator is initialized using system clock, e.g
         # Use random state instance to generate 1000 random numbers
         # from discrete uniform distribution [1, 6]
         isample = rs_def.randint(1, 6 + 1, size=1000)
+
+Different members of the set of generators initialized with the same seed are designed to generate
+statistically independent streams of randomness. This property makes MT2203 generator suitable for 
+parallelizing stochastic algorithms. Please refer to "examples/" folder in the `GitHub repo
+<https://github.com/IntelPython/mkl_random>`_.
+
+.. _whrng: https://spec.oneapi.io/versions/1.0-rev-2/elements/oneMKL/source/domains/rng/mkl-rng-wichmann_hill.html