diff --git a/.github/workflows/build-docs.yml b/.github/workflows/build-docs.yml
new file mode 100644
index 0000000..ae990c4
--- /dev/null
+++ b/.github/workflows/build-docs.yml
@@ -0,0 +1,93 @@
+name: Build mkl_random documentation
+on:
+  pull_request:
+  push:
+    branches: [master]
+
+permissions: read-all
+
+jobs:
+  build-and-deploy:
+    name: Build and Deploy Documentation
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+      pull-requests: write
+    steps:
+      - name: Cancel Previous Runs
+        uses: styfle/cancel-workflow-action@0.12.1
+        with:
+          access_token: ${{ github.token }}
+      - name: Add Intel repository
+        if: ${{ !github.event.pull_request || github.event.action != 'closed' }}
+        run: |
+          wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2023.PUB
+          sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS-2023.PUB
+          rm GPG-PUB-KEY-INTEL-SW-PRODUCTS-2023.PUB
+          sudo add-apt-repository "deb https://apt.repos.intel.com/oneapi all main"
+          sudo apt-get update
+      - name: Install Intel OneAPI
+        if: ${{ !github.event.pull_request || github.event.action != 'closed' }}
+        run: |
+          sudo apt-get install intel-oneapi-compiler-dpcpp-cpp intel-oneapi-mkl-devel
+      - name: Setup Python
+        if: ${{ !github.event.pull_request || github.event.action != 'closed' }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+          architecture: x64
+      - name: Install sphinx dependencies
+        if: ${{ !github.event.pull_request || github.event.action != 'closed' }}
+        shell: bash -l {0}
+        run: |
+          pip install numpy cython setuptools scikit-build cmake sphinx sphinx_rtd_theme furo pydot graphviz sphinxcontrib-programoutput sphinxcontrib-googleanalytics sphinx_design
+      - name: Checkout repo
+        uses: actions/checkout@v4.1.1
+        with:
+          fetch-depth: 0
+          persist-credentials: false
+      - name: Build mkl_random+docs
+        if: ${{ !github.event.pull_request || github.event.action != 'closed' }}
+        shell: bash -l {0}
+        run: |
+          # Ensure that SYCL libraries are on LD_LIBRARY_PATH
+          source /opt/intel/oneapi/setvars.sh
+          python setup.py develop	  
+          python -c "import mkl_random; print(mkl_random.__version__)" || exit 1
+          sphinx-build -M html docs/source docs/build
+          mkdir -p ~/rendered_docs
+          cp -r docs/build/html/* ~/rendered_docs/
+          git clean -dfx
+      - name: Save built docs as an artifact
+        if: ${{ github.event.pull_request && github.event.action != 'closed'}}
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ env.PACKAGE_NAME }} rendered documentation
+          path: ~/rendered_docs
+      - name: Configure git
+        if: ${{ !github.event.pull_request && github.event.action != 'closed'}}
+        run: |
+          git config --local user.email "scripting@intel.com"
+          git config --local user.name "mkl_random-doc-bot"
+        timeout-minutes: 5
+      - name: Checkout gh-pages
+        if: ${{ !github.event.pull_request && github.event.action != 'closed'}}
+        run: |
+          git fetch --all
+          git checkout gh-pages
+      - name: 'Copy build to root'
+        if: ${{ !github.event.pull_request && github.event.action != 'closed'}}
+        run: |
+          cp -R ~/rendered_docs/* .
+        timeout-minutes: 10
+      - name: 'Commit changes'
+        if: ${{ !github.event.pull_request && github.event.action != 'closed'}}
+        run: |
+          git add . && git commit -m "Deploy: ${{ github.sha }}"
+        continue-on-error: true
+        timeout-minutes: 10
+      - name: Publish changes
+        if: ${{ success() && !github.event.pull_request && github.event.action != 'closed'}}
+        run: |
+          git push origin gh-pages
+        timeout-minutes: 10
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..f65eca4
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,7 @@
+*~
+build/
+mkl_random/__pycache__/
+mkl_random/tests/__pycache__/
+mkl_random/mklrand.cpp
+mkl_random/mklrand.cpython*.so
+mkl_random.egg-info/
diff --git a/docs/Makefile b/docs/Makefile
new file mode 100644
index 0000000..d0c3cbf
--- /dev/null
+++ b/docs/Makefile
@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = source
+BUILDDIR      = build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/docs/make.bat b/docs/make.bat
new file mode 100644
index 0000000..747ffb7
--- /dev/null
+++ b/docs/make.bat
@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=source
+set BUILDDIR=build
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.https://www.sphinx-doc.org/
+	exit /b 1
+)
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd
diff --git a/docs/source/conf.py b/docs/source/conf.py
new file mode 100644
index 0000000..faf2b40
--- /dev/null
+++ b/docs/source/conf.py
@@ -0,0 +1,41 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# For the full list of built-in configuration values, see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Project information -----------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
+
+project = 'mkl_random'
+copyright = '2017-2024, Intel Corp.'
+author = 'Intel Corp.'
+release = '1.2.5'
+
+# -- General configuration ---------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
+
+extensions = [
+    "sphinx.ext.autodoc",
+    "sphinx.ext.autosummary",
+    "sphinx.ext.coverage",
+    "sphinx.ext.extlinks",
+    "sphinx.ext.intersphinx",
+    "sphinx.ext.githubpages",
+    "sphinx.ext.napoleon",
+    "sphinx.ext.todo",
+    "sphinx.ext.viewcode",
+    "sphinxcontrib.programoutput",
+    # "sphinxcontrib.googleanalytics",
+    'sphinx_design',
+]
+
+templates_path = ['_templates']
+exclude_patterns = []
+
+
+
+# -- Options for HTML output -------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
+
+html_theme = 'furo'
+html_static_path = ['_static']
diff --git a/docs/source/how_to.rst b/docs/source/how_to.rst
new file mode 100644
index 0000000..809a47f
--- /dev/null
+++ b/docs/source/how_to.rst
@@ -0,0 +1,4 @@
+How-to Guides
+=============
+
+To be written.
\ No newline at end of file
diff --git a/docs/source/index.rst b/docs/source/index.rst
new file mode 100644
index 0000000..33149a8
--- /dev/null
+++ b/docs/source/index.rst
@@ -0,0 +1,73 @@
+Random sampling powered by Intel(R) Math Kernel Library 
+=======================================================
+
+:mod:`mkl_random` is Python package exposing pseudo-random and non-deterministic random 
+number generators with continuous and discrete distribution available in 
+Intel(R) Math Kernel Library (MKL).
+
+.. grid:: 2
+    :gutter: 3
+
+    .. grid-item-card:: Beginner Guides
+
+        New to :mod:`mkl_random`? Check out the Tutorials.
+        They are a hands-on introduction for beginners.
+
+        +++
+
+        .. button-ref:: tutorials
+            :expand:
+            :color: secondary
+            :click-parent:
+
+            To the beginner guides
+
+    .. grid-item-card:: User Guides
+
+        The user guides are recipes for key tasks and common problems.
+
+        +++
+
+        .. button-ref:: how_to
+            :expand:
+            :color: secondary
+            :click-parent:
+
+            To the user guides
+
+    .. grid-item-card:: Reference Guide
+
+        The reference guide contains a detailed description of class :class:`mkl_random.RandomState` and its methods.
+
+        +++
+
+        .. button-ref:: reference/index
+            :expand:
+            :color: secondary
+            :click-parent:
+
+            To the reference guide
+
+    .. grid-item-card:: Contributor Guides
+
+        Want to add to the codebase?
+        The contributing guidelines will guide you through the
+        process of improving :mod:`mkl_random`.
+
+        +++
+
+        .. button-ref:: maintenance/index
+            :expand:
+            :color: secondary
+            :click-parent:
+
+            To the contributor guides
+
+
+.. toctree::
+    :hidden:
+
+    tutorials
+    how_to
+    reference/index
+    maintenance/index
diff --git a/docs/source/maintenance/index.rst b/docs/source/maintenance/index.rst
new file mode 100644
index 0000000..2611957
--- /dev/null
+++ b/docs/source/maintenance/index.rst
@@ -0,0 +1,50 @@
+Contributing
+============
+
+:mod:`mkl_random` is an free and open source project. 
+We welcome and appreciate your contributions.
+
+To contribute, fork the repo https://github.com/IntelPython/mkl_random.git,
+clone it:
+
+.. code-block::
+    :caption: How to clone the repo
+
+         git clone https://github.com/<fork-org>/mkl_random.git
+
+
+A working compiler is needed build :mod:`mkl_random`. 
+Both Gnu :code:`g++` and Intel LLVM :code:`icpx` are supported.
+
+Make sure to install Python packages required to build :mod:`mkl_random`:
+
+* :mod:`python`
+* :mod:`numpy`
+* :mod:`cython`
+* :mod:`setuptools`
+
+You would also need Intel(R) MKL library and its headers. Set :code:`MKLROOT` environment
+variable so that :code:`${MKLROOT}/include/mkl.h` and :code:`${MKLROOT}/lib/libmkl_rt.so`
+can be found.
+
+.. code-block:: bash
+   :caption: Building mkl_random
+
+      $ export MKLROOT=</path/to/mkl>
+      python setup.py develop
+
+To run test suite, install :mod:`pytest`, and run
+
+.. code-block:: bash
+   :caption: Running mkl_random test suite
+
+      python -m pytest mkl_random/tests
+
+To build documentation, install dependencies and running
+
+.. code-block:: bash
+   :caption: Building mkl_random documentation
+
+      $ sphinx-build -M html docs/source docs/build
+
+Rendered documentation can be found in "docs/build/html".
\ No newline at end of file
diff --git a/docs/source/reference/api.rst b/docs/source/reference/api.rst
new file mode 100644
index 0000000..cf2f084
--- /dev/null
+++ b/docs/source/reference/api.rst
@@ -0,0 +1,7 @@
+.. _fullapi:
+
+Class RandomState
+=================
+
+.. autoclass:: mkl_random.RandomState
+    :members:
diff --git a/docs/source/reference/ars5.rst b/docs/source/reference/ars5.rst
new file mode 100644
index 0000000..0a91c02
--- /dev/null
+++ b/docs/source/reference/ars5.rst
@@ -0,0 +1,37 @@
+ARS5 brng
+=========
+
+The ARS5 counter-based pseudorandom number generator based on AES encryption algorithm can be 
+initialized with either an integral seed, a list of integral seeds, or automatically.
+
+.. code-block:: python
+    :caption: Construction for ARS5 basic random number generator with scalar seed
+
+        import mkl_random
+        rs = mkl_random.RandomState(1234, brng="ars5")
+
+        # Use random state instance to generate 1000 random numbers from
+        # Uniform(0, 1) distribution
+        esample = rs.uniform(0, 1, size=1000)
+
+.. code-block:: python
+    :caption: Construction for ARS5 basic random number generator with vector seed
+
+        import mkl_random
+        rs_vec = mkl_random.RandomState([1234, 567, 89, 0], brng="ars5")
+
+        # Use random state instance to generate 1000 random numbers from
+        # Gamma(3, 1) distibution
+        gsample = rs_vec.gamma(3, 1, size=1000)
+
+When seed is not specified, the generator is initialized using system clock, e.g.:
+
+.. code-block:: python
+    :caption: Construction for ARS5 basic random number generator automatic seed
+
+        import mkl_random
+        rs_def = mkl_random.RandomState(brng="ars5")
+
+        # Use random state instance to generate 1000 random numbers
+        # from discrete uniform distribution [1, 6]
+        isample = rs_def.randint(1, 6 + 1, size=1000)
diff --git a/docs/source/reference/index.rst b/docs/source/reference/index.rst
new file mode 100644
index 0000000..c66d54b
--- /dev/null
+++ b/docs/source/reference/index.rst
@@ -0,0 +1,39 @@
+:mod:`mkl_random` APIs
+======================
+
+The class :doc:`mkl_random.RandomState <./api>` exposes sampling from probability distributions while supporting
+different streams of randomness, also known as basic random number generators.
+
+The basic random number generator is chosen by specifying :code:`brng` keyword argument to the constructor of :code:`mkl.RandomState` class.
+
+The list of supported basic random number generators is as follows (also see `oneMKL Engines <oneMKLBRNG_>`_):
+
+* :code:`'MT19937'` - the Mersenne Twister pseudo-random number generator (default), :doc:`example <mt19937>`
+* :code:`'SFMT19937'` - the SIMD-oriented Mersenne Twister pseudo-random number generator, :doc:`example <sfmt19937>`
+* :code:`'MT2203'` -  the set of 6024 Mersenne Twister pseudorandom number generators, :doc:`example <mt2203>`
+* :code:`'R250'` - the 32-bit generalized feedback shift register pseudorandom number generator GFSR(250,103), :doc:`example <r250>`
+* :code:`'WH'` -  the set of 273 Wichmann-Hill’s combined multiplicative congruential generators, :doc:`example <wichmann_hill>`
+* :code:`'MCG31'` - the 31-bit multiplicative congruential pseudorandom number generator, :doc:`example <mcg31>`
+* :code:`'MCG59'` - the 59-bit multiplicative congruential pseudorandom number generator, :doc:`example <mcg59>`
+* :code:`'MRG32K3A'` - the combined multiple recursive pseudorandom number generator MRG32k3a, :doc:`example <mrg32k3a>`
+* :code:`'PHILOX4X32X10'` - the Philox4x32x10 counter-based pseudorandom number generator, :doc:`example <philox4x32x10>`
+* :code:`'NONDETERM'` - the generator with non-deterministic source of randomness (for example, a hardware device), :doc:`example <nondeterministic>`
+* :code:`'ARS5'` - the ARS5 counter-based pseudorandom number generator based on AES encryption algorithm, :doc:`example <ars5>`
+
+.. _oneMKLBRNG: https://spec.oneapi.io/versions/1.0-rev-2/elements/oneMKL/source/domains/rng/engines-basic-random-number-generators.html
+
+.. toctree::
+    :hidden:
+
+    api
+    mt19937
+    sfmt19937
+    r250
+    mt2203
+    wichmann_hill
+    mcg31
+    mcg59
+    mrg32k3a
+    philox4x32x10
+    nondeterministic
+    ars5
diff --git a/docs/source/reference/mcg31.rst b/docs/source/reference/mcg31.rst
new file mode 100644
index 0000000..87ee301
--- /dev/null
+++ b/docs/source/reference/mcg31.rst
@@ -0,0 +1,37 @@
+MCG31 brng
+==========
+
+The 31-bit multiplicative congruential pseudorandom number generator MCG(1132489760, 2**31 -1) can be 
+initialized with  either an integral seed, a list of integral seeds, or automatically.
+
+.. code-block:: python
+    :caption: Construction for MCG31 basic random number generator with scalar seed
+
+        import mkl_random
+        rs = mkl_random.RandomState(1234, brng="MCG31")
+
+        # Use random state instance to generate 1000 random numbers from
+        # Uniform(0, 1) distribution
+        esample = rs.uniform(0, 1, size=1000)
+
+.. code-block:: python
+    :caption: Construction for MCG31 basic random number generator with vector seed
+
+        import mkl_random
+        rs_vec = mkl_random.RandomState([1234, 567, 89, 0], brng="MCG31")
+
+        # Use random state instance to generate 1000 random numbers from
+        # Gamma(3, 1) distibution
+        gsample = rs_vec.gamma(3, 1, size=1000)
+
+When seed is not specified, the generator is initialized using system clock, e.g.:
+
+.. code-block:: python
+    :caption: Construction for MCG31 basic random number generator automatic seed
+
+        import mkl_random
+        rs_def = mkl_random.RandomState(brng="MCG31")
+
+        # Use random state instance to generate 1000 random numbers
+        # from discrete uniform distribution [1, 6]
+        isample = rs_def.randint(1, 6 + 1, size=1000)
diff --git a/docs/source/reference/mcg59.rst b/docs/source/reference/mcg59.rst
new file mode 100644
index 0000000..314361a
--- /dev/null
+++ b/docs/source/reference/mcg59.rst
@@ -0,0 +1,37 @@
+MCG59 brng
+==========
+
+The 59-bit multiplicative congruential pseudorandom number generator can be 
+initialized with  either an integral seed, a list of integral seeds, or automatically.
+
+.. code-block:: python
+    :caption: Construction for MCG31 basic random number generator with scalar seed
+
+        import mkl_random
+        rs = mkl_random.RandomState(1234, brng="MCG59")
+
+        # Use random state instance to generate 1000 random numbers from
+        # Uniform(0, 1) distribution
+        esample = rs.uniform(0, 1, size=1000)
+
+.. code-block:: python
+    :caption: Construction for MCG31 basic random number generator with vector seed
+
+        import mkl_random
+        rs_vec = mkl_random.RandomState([1234, 567, 89, 0], brng="MCG59")
+
+        # Use random state instance to generate 1000 random numbers from
+        # Gamma(3, 1) distibution
+        gsample = rs_vec.gamma(3, 1, size=1000)
+
+When seed is not specified, the generator is initialized using system clock, e.g.:
+
+.. code-block:: python
+    :caption: Construction for MCG31 basic random number generator automatic seed
+
+        import mkl_random
+        rs_def = mkl_random.RandomState(brng="MCG59")
+
+        # Use random state instance to generate 1000 random numbers
+        # from discrete uniform distribution [1, 6]
+        isample = rs_def.randint(1, 6 + 1, size=1000)
diff --git a/docs/source/reference/mrg32k3a.rst b/docs/source/reference/mrg32k3a.rst
new file mode 100644
index 0000000..54c5c7e
--- /dev/null
+++ b/docs/source/reference/mrg32k3a.rst
@@ -0,0 +1,37 @@
+MRG32k3a brng
+=============
+
+The combined multiple recursive pseudorandom number generator MRG32k3a can be 
+initialized with  either an integral seed, a list of integral seeds, or automatically.
+
+.. code-block:: python
+    :caption: Construction for MRG32k3a basic random number generator with scalar seed
+
+        import mkl_random
+        rs = mkl_random.RandomState(1234, brng="MRG32k3a")
+
+        # Use random state instance to generate 1000 random numbers from
+        # Uniform(0, 1) distribution
+        esample = rs.uniform(0, 1, size=1000)
+
+.. code-block:: python
+    :caption: Construction for MRG32k3a basic random number generator with vector seed
+
+        import mkl_random
+        rs_vec = mkl_random.RandomState([1234, 567, 89, 0], brng="MRG32k3a")
+
+        # Use random state instance to generate 1000 random numbers from
+        # Gamma(3, 1) distibution
+        gsample = rs_vec.gamma(3, 1, size=1000)
+
+When seed is not specified, the generator is initialized using system clock, e.g.:
+
+.. code-block:: python
+    :caption: Construction for MRG32k3a basic random number generator automatic seed
+
+        import mkl_random
+        rs_def = mkl_random.RandomState(brng="MRG32k3a")
+
+        # Use random state instance to generate 1000 random numbers
+        # from discrete uniform distribution [1, 6]
+        isample = rs_def.randint(1, 6 + 1, size=1000)
diff --git a/docs/source/reference/mt19937.rst b/docs/source/reference/mt19937.rst
new file mode 100644
index 0000000..6b32777
--- /dev/null
+++ b/docs/source/reference/mt19937.rst
@@ -0,0 +1,35 @@
+MT19937 brng
+============
+
+The Mersenne Twister random number generator can be initialized with either an integral seed, 
+a list of integral seeds, or automatically.
+
+.. code-block:: python
+    :caption: Construction for MT19937 basic random number generator with scalar seed
+
+        import mkl_random
+        rs = mkl_random.RandomState(1234, brng="MT19937")
+
+        # Use random state instance to generate 1000 uniform random numbers
+        usample = rs.uniform(0, 1, size=1000)
+
+.. code-block:: python
+    :caption: Construction for MT19937 basic random number generator with vector seed
+
+        import mkl_random
+        rs_vec = mkl_random.RandomState([1234, 567, 89, 0], brng="MT19937")
+
+        # Use random state instance to generate 1000 Gaussian random numbers
+        nsample = rs_vec.normal(0, 1, size=1000)
+
+When seed is not specified, the generator is initialized using system clock, e.g.:
+
+.. code-block:: python
+    :caption: Construction for MT19937 basic random number generator automatic seed
+
+        import mkl_random
+        rs_def = mkl_random.RandomState(brng="MT19937")
+
+        # Use random state instance to generate 1000 random numbers
+        # from Binomial(10, 0.4)
+        bsample = rs_def.binomial(10, 0.4, size=1000)
diff --git a/docs/source/reference/mt2203.rst b/docs/source/reference/mt2203.rst
new file mode 100644
index 0000000..6bf43a5
--- /dev/null
+++ b/docs/source/reference/mt2203.rst
@@ -0,0 +1,53 @@
+MT2203 brng
+===========
+
+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} < 6024`.
+
+.. code-block:: python
+    :caption: Construction for MT2203 basic random number generator with scalar seed
+
+        import mkl_random
+        seed = 777
+        # initialize representative generator from the set
+        rs0 = mkl_random.RandomState(seed, brng="MT2203")
+
+        # initialize 0-th member of the set
+        rs0 = mkl_random.RandomState(seed, brng=("MT2203", 0))
+
+        # initialize 5-th member of the set
+        rs5 = mkl_random.RandomState(seed, brng=("MT2203", 5))
+
+        sample = rs5.uniform(0, 1, size=1_000_000)
+
+.. code-block:: python
+    :caption: Construction for MT2203 basic random number generator with vector seed
+
+        import mkl_random
+        rs = mkl_random.RandomState([1234, 567, 89, 0], brng=("MT2203", 6023))
+
+        # Use random state instance to generate 1000 random numbers from
+        # Gamma(3, 1) distibution
+        gsample = rs_vec.gamma(3, 1, size=1000)
+
+When seed is not specified, the generator is initialized using system clock, e.g.:
+
+.. code-block:: python
+    :caption: Construction for MT2203 basic random number generator automatic seed
+
+        import mkl_random
+        rs_def = mkl_random.RandomState(brng="MT2203")
+
+        # 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
\ No newline at end of file
diff --git a/docs/source/reference/nondeterministic.rst b/docs/source/reference/nondeterministic.rst
new file mode 100644
index 0000000..a3b2212
--- /dev/null
+++ b/docs/source/reference/nondeterministic.rst
@@ -0,0 +1,18 @@
+Nondeterm brng
+==============
+
+The generator with non-deterministic source of randomness, such as a hardware device, requires no seeding.
+This basic random number generator should not be used if reproducibility of stochastic simulation is required.
+
+.. code-block:: python
+    :caption: Construction for non-deterministic basic random number generator
+
+        import mkl_random
+        rs = mkl_random.RandomState(brng="nondeterm")
+
+        # Use random state instance to generate 1000 random numbers from
+        # Uniform(0, 1) distribution
+        esample = rs.uniform(0, 1, size=1000)
+
+Seed parameter provided to the constructor of :class:`mkl_random.RandomState`,
+or :meth:`mkl_random.RandomState.seed` is ignored.
\ No newline at end of file
diff --git a/docs/source/reference/philox4x32x10.rst b/docs/source/reference/philox4x32x10.rst
new file mode 100644
index 0000000..9eb5ae1
--- /dev/null
+++ b/docs/source/reference/philox4x32x10.rst
@@ -0,0 +1,37 @@
+Philox4x32x10 brng
+==================
+
+The Philox 4x32x10 counter-based pseudorandom number generator can be 
+initialized with  either an integral seed, a list of integral seeds, or automatically.
+
+.. code-block:: python
+    :caption: Construction for Philox4x32x10 basic random number generator with scalar seed
+
+        import mkl_random
+        rs = mkl_random.RandomState(1234, brng="philox4x32x10")
+
+        # Use random state instance to generate 1000 random numbers from
+        # Uniform(0, 1) distribution
+        esample = rs.uniform(0, 1, size=1000)
+
+.. code-block:: python
+    :caption: Construction for Philox4x32x10 basic random number generator with vector seed
+
+        import mkl_random
+        rs_vec = mkl_random.RandomState([1234, 567, 89, 0], brng="philox4x32x10")
+
+        # Use random state instance to generate 1000 random numbers from
+        # Gamma(3, 1) distibution
+        gsample = rs_vec.gamma(3, 1, size=1000)
+
+When seed is not specified, the generator is initialized using system clock, e.g.:
+
+.. code-block:: python
+    :caption: Construction for Philox4x32x10 basic random number generator automatic seed
+
+        import mkl_random
+        rs_def = mkl_random.RandomState(brng="philox4x32x10")
+
+        # Use random state instance to generate 1000 random numbers
+        # from discrete uniform distribution [1, 6]
+        isample = rs_def.randint(1, 6 + 1, size=1000)
diff --git a/docs/source/reference/r250.rst b/docs/source/reference/r250.rst
new file mode 100644
index 0000000..5f1c5a5
--- /dev/null
+++ b/docs/source/reference/r250.rst
@@ -0,0 +1,37 @@
+R250 brng
+=========
+
+The 32-bit generalized feedback shift register pseudorandom number generator GFSR(250,103) can be 
+initialized with  either an integral seed, a list of integral seeds, or automatically.
+
+.. code-block:: python
+    :caption: Construction for R250 basic random number generator with scalar seed
+
+        import mkl_random
+        rs = mkl_random.RandomState(1234, brng="R250")
+
+        # Use random state instance to generate 1000 random numbers from
+        # Uniform(0, 1) distribution
+        esample = rs.uniform(0, 1, size=1000)
+
+.. code-block:: python
+    :caption: Construction for R250 basic random number generator with vector seed
+
+        import mkl_random
+        rs_vec = mkl_random.RandomState([1234, 567, 89, 0], brng="R250")
+
+        # Use random state instance to generate 1000 random numbers from
+        # Gamma(3, 1) distibution
+        gsample = rs_vec.gamma(3, 1, size=1000)
+
+When seed is not specified, the generator is initialized using system clock, e.g.:
+
+.. code-block:: python
+    :caption: Construction for R250 basic random number generator automatic seed
+
+        import mkl_random
+        rs_def = mkl_random.RandomState(brng="R250")
+
+        # Use random state instance to generate 1000 random numbers
+        # from discrete uniform distribution [1, 6]
+        isample = rs_def.randint(1, 6 + 1, size=1000)
diff --git a/docs/source/reference/sfmt19937.rst b/docs/source/reference/sfmt19937.rst
new file mode 100644
index 0000000..db6c53f
--- /dev/null
+++ b/docs/source/reference/sfmt19937.rst
@@ -0,0 +1,37 @@
+SFMT19937 brng
+==============
+
+The SIMD-oriented Mersenne Twister random number generator can be initialized with 
+either an integral seed, a list of integral seeds, or automatically.
+
+.. code-block:: python
+    :caption: Construction for SFMT19937 basic random number generator with scalar seed
+
+        import mkl_random
+        rs = mkl_random.RandomState(1234, brng="SFMT19937")
+
+        # Use random state instance to generate 1000 random numbers from
+        # Exponential distribution
+        esample = rs.exponential(2.3, size=1000)
+
+.. code-block:: python
+    :caption: Construction for SFMT19937 basic random number generator with vector seed
+
+        import mkl_random
+        rs_vec = mkl_random.RandomState([1234, 567, 89, 0], brng="SFMT19937")
+
+        # Use random state instance to generate 1000 random numbers from
+        # Gamma distibution
+        gsample = rs_vec.gamma(3, 1, size=1000)
+
+When seed is not specified, the generator is initialized using system clock, e.g.:
+
+.. code-block:: python
+    :caption: Construction for SFMT19937 basic random number generator automatic seed
+
+        import mkl_random
+        rs_def = mkl_random.RandomState(brng="SFMT19937")
+
+        # Use random state instance to generate 1000 random numbers
+        # from discrete uniform distribution [1, 6]
+        isample = rs_def.randint(1, 6 + 1, size=1000)
diff --git a/docs/source/reference/wichmann_hill.rst b/docs/source/reference/wichmann_hill.rst
new file mode 100644
index 0000000..b0128dc
--- /dev/null
+++ b/docs/source/reference/wichmann_hill.rst
@@ -0,0 +1,54 @@
+Wichmann-Hill brng
+==================
+
+Each generator from the set of 273 Wichmann-Hill’s combined multiplicative congruential 
+`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} < 273`.
+
+.. code-block:: python
+    :caption: Construction for WH basic random number generator with scalar seed
+
+        import mkl_random
+        seed = 777
+        # initialize representative generator from the set
+        rs0 = mkl_random.RandomState(seed, brng="WH")
+
+        # initialize 0-th member of the set
+        rs0 = mkl_random.RandomState(seed, brng=("WH", 0))
+
+        # initialize 5-th member of the set
+        rs5 = mkl_random.RandomState(seed, brng=("WH", 5))
+
+        sample = rs5.uniform(0, 1, size=1_000_000)
+
+.. code-block:: python
+    :caption: Construction for WH basic random number generator with vector seed
+
+        import mkl_random
+        rs = mkl_random.RandomState([1234, 567, 89, 0], brng=("WH", 200))
+
+        # Use random state instance to generate 1000 random numbers from
+        # Gamma(3, 1) distibution
+        gsample = rs_vec.gamma(3, 1, size=1000)
+
+When seed is not specified, the generator is initialized using system clock, e.g.:
+
+.. code-block:: python
+    :caption: Construction for WH basic random number generator automatic seed
+
+        import mkl_random
+        rs_def = mkl_random.RandomState(brng="WH")
+
+        # 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
\ No newline at end of file
diff --git a/docs/source/tutorials.rst b/docs/source/tutorials.rst
new file mode 100644
index 0000000..faeb59a
--- /dev/null
+++ b/docs/source/tutorials.rst
@@ -0,0 +1,93 @@
+Installation
+============
+
+Package :mod:`mkl_random` is available in conda ecosystem on "conda-forge", default, and "intel" channels.
+
+.. code-block:: bash
+    :caption: Install mkl_random from conda-forge channel
+
+        $ conda install -c conda-forge mkl_random
+
+.. code-block:: bash
+    :caption: Install mkl_random from intel channel
+
+        $ conda install -c intel mkl_random
+
+.. code-block:: bash
+    :caption: Install mkl_random from default
+
+        $ conda install mkl_random
+
+The package can also be installed via :code:`pip`, either from PyPI, or from 
+
+.. code-block:: bash
+    :caption: Install mkl_random using pip from intel channel on Anaconda
+
+    $ pip install -i https://pypi.anaconda.org/intel/simple mkl_random
+
+.. code-block:: bash
+    :caption: Install mkl_random using pip from PyPI
+
+    $ pip install mkl_random
+
+The :mod:`mkl_random` is also distributed as part of `Intel(R) Distribution for Python* <https://www.intel.com/content/www/us/en/developer/tools/oneapi/distribution-for-python.html>`_.
+
+Beginner's guide
+================
+
+The :mod:`mkl_random` package was designed following :class:`numpy.random.RandomState` class to 
+make use of :mod:`mkl_random` easy for current uses of :mod:`numpy.random` module.
+
+.. note::
+    Since the first release of `mkl_random`, NumPy introduced new classes :class:`numpy.random.Generator` and 
+    :class:`numpy.random.BitGenerator`, while also retaining :class:`numpy.random.RandomState` for backwards
+    compatibility.
+
+The state of pseudo-random number generator is stored in :class:`mkl_random.RandomState` class, 
+so using :mod:`mkl_random` begins with creating an instance of this class:
+
+.. code-block:: python
+    :caption: Construct random number generator
+
+        import mkl_random
+        rs = mkl_random.RandomState(seed=1234)
+
+Sampling from difference probability distribution is done by calling class methods on the constructed instance:
+
+.. code-block:: python
+    :caption: Generate one million variates from standard continuous uniform distribution
+
+        s = rs.uniform(0, 1, size=1_000_000)
+
+Drawing samples updates the state of pseudo-random number generator so that next sample is statistically 
+independent from the previous one (with caveats of using pseudo-random generators implied). 
+
+Here is an example of estimating value of :math:`\pi` by using Monte-Carlo method:
+
+.. code-block:: python
+    :caption: Using Monte-Carlo method to estimate value of pi
+
+        import numpy as np
+        import mkl_random
+ 
+        rs = mkl_random.RandomState(seed=1234)
+
+        n = 10**8
+        batch_size = 10**6
+        accepted = 0
+        sampled = 0
+        while sampled < n:
+            sampled += batch_size
+            x = rs.uniform(0, 1, size=batch_size)
+            y = rs.uniform(0, 1, size=batch_size)
+            accepted += np.sum(x*x + y*y < 1.0)
+        
+        print("Pi estimate: ", 4. * (accepted / n))
+
+Sample output of running such an example:
+
+.. code-block:: bash
+    :caption: Sample output after executing above script
+
+        $ python pi.py
+        Pi estimate:  3.14167732
\ No newline at end of file