Merge branch 'develop' of github.com:QMCSoftware/QMCSoftware into dev…

…elop
QMCSoftware · Apr 20, 2022 · f1bc37a · f1bc37a
2 parents 4616f53 + 7c8277b
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diff --git a/demos/MC_vs_QMC.ipynb b/demos/MC_vs_QMC.ipynb
@@ -9,6 +9,13 @@
     "Monte Carlo algorithms work on independent identically distributed (IID) points while Quasi-Monte Carlo algorithms work on low discrepancy (LD) sequences. LD generators, such as those for the lattice and Sobol' sequences, provide samples whose space filling properties can be exploited by Quasi-Monte Carlo algorithms."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/MC_vs_QMC.ipynb)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

diff --git a/demos/asian-option-mlqmc.ipynb b/demos/asian-option-mlqmc.ipynb
@@ -27,6 +27,13 @@
     "# Comparison of multilevel (Quasi-)Monte Carlo for an Asian option problem"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/asian-option-mlqmc.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

diff --git a/demos/control_variates.ipynb b/demos/control_variates.ipynb
@@ -11,6 +11,13 @@
     "This notebook demonstrates QMCPy's current support for control variates. "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/control_variates.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {

diff --git a/demos/gaussian_diagnostics_demo.ipynb b/demos/gaussian_diagnostics_demo.ipynb
@@ -9,6 +9,13 @@
     "Experiments to demonstate Guassian assumption used in `cubBayesLattice`"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/gaussian_diagnostics_demo.ipynb)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 47,

diff --git a/demos/importance_sampling.ipynb b/demos/importance_sampling.ipynb
@@ -7,6 +7,13 @@
     "# Importance Sampling Examples"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/importance_sampling.ipynb)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

diff --git a/demos/lebesgue_integration.ipynb b/demos/lebesgue_integration.ipynb
@@ -8,6 +8,13 @@
     "This notebook will give examples of how to use QMCPy for integration problems that not are defined in terms of a standard measure. i.e. Uniform or Gaussian. "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/lebesgue_integration.ipynb)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

diff --git a/demos/qmcpy_intro.ipynb b/demos/qmcpy_intro.ipynb
@@ -8,6 +8,13 @@
     "## Importing QMCPy"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/qmcpy_intro.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

diff --git a/demos/quickstart.ipynb b/demos/quickstart.ipynb
@@ -12,6 +12,13 @@
     "In this tutorial, we introduce QMCPy [1]  by an example. QMCPy can be installed with **pip install qmcpy** or cloned from the  [QMCSoftware GitHub repository](https://github.com/QMCSoftware/QMCSoftware)."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/quickstart.ipynb)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {