diff --git "a/assignment-1/assignment_1/AI_Assignment_1.ipynb" "b/assignment-1/assignment_1/AI_Assignment_1.ipynb"
deleted file mode 100644--- "a/assignment-1/assignment_1/AI_Assignment_1.ipynb"
+++ /dev/null
@@ -1,1281 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "id": "Ff0nasoRGGbR",
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "#import jax"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "0B5TJR9BZbM9",
-    "tags": []
-   },
-   "source": [
-    "# Question 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "1q0PuluDUsXD",
-    "tags": []
-   },
-   "source": [
-    "## Question 1a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "I7NKm5-Ewj5N"
-   },
-   "source": [
-    "Sum of bernoulli random variables is equal to binomial distribution, so that's what's being used here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "0GgQD7nPpRRk"
-   },
-   "source": [
-    "Your job is to sample n=50 posts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "y8U2pgoYGMvm",
-    "outputId": "7e4f28c7-f2c9-4349-81cf-f29dd73e2ff8",
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "22"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "n = 50\n",
-    "p = 0.5\n",
-    "y = np.random.binomial(n=n, p=p)\n",
-    "samples = np.concatenate((np.zeros(y), np.ones(n-y)))\n",
-    "y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "moP7bGGppWAz"
-   },
-   "source": [
-    "\n",
-    "and estimate the approval rate of the CEO by considering the statistics of the approval rate of the CEO by considering the statistics of $y = \\Sigma_{i=0}^{n}x_i$. \n",
-    "\n",
-    "Statistics:  \n",
-    "- $\\bar{x}=\\frac{Σ_{i=0}^{n}x_i}{n}$\n",
-    "- $\\sigma^2=\\frac{Σ_{i=0}^{n}(x_i-\\bar{x})^2}{n-1}→\\sigma=\\sqrt{\\frac{Σ_{i=0}^{n}(x_i-\\bar{x})^2}{n-1}}$ "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "A0th2VLVUsAd",
-    "outputId": "36f67974-5f36-45bd-aa8f-9f3ca5780332",
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "sample mean \"xbar\" is: 0.44\n",
-      "sample stdev \"S\" is: 0.515870573864794\n"
-     ]
-    }
-   ],
-   "source": [
-    "xbar = y/n\n",
-    "print(f\"sample mean \\\"xbar\\\" is: {xbar}\")\n",
-    "#\n",
-    "S = np.sqrt(np.sum([(xi - xbar)**2 for xi in samples])/(n-1))\n",
-    "print(f\"sample stdev \\\"S\\\" is: {S}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "MC63GJqFqk_p"
-   },
-   "source": [
-    "Probability that 25 employees approve of the CEO: $P(y=25)= {50 \\choose 25}p^k (1-p)^{(n-k)}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "SnT5Kr1tX5vr",
-    "outputId": "ae9d0942-e86b-45e3-ca58-ee37a69f8c3d",
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.11227517265921705"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# What is the probability that 25 employees approve the CEO?\n",
-    "k = 25\n",
-    "(choose := (np.math.factorial(n)/(np.math.factorial(n-k)*np.math.factorial(k))))*(p**k)*((1-p)**(n-k))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "CcIhV6T8RH4t",
-    "tags": []
-   },
-   "source": [
-    "## Question 1b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "3PfFNMf2Yqxb"
-   },
-   "source": [
-    "$z = \\frac{y-\\mu_y}{\\sigma_y}$, by central limit theorem implies that $z \\sim \\mathcal{N}(\\mu_y, \\sigma^2_y)$ where $\\mu_y = 0 \\land \\sigma_y^2 = 1$. This gives that $(P(y = 25/50)|Y\\sim\\mathcal{B}(n,p))  ≃ = P(\\frac{24.5 - 25} \\leq z=\\frac{25/50 - 0}{\\sqrt{1}} \\geq 25.5)$ \n",
-    "\n",
-    "We find then the probability of that interval:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-0.1414213562373095, 0.1414213562373095)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mu = p*n\n",
-    "sigma = np.sqrt(p*(1-p)*n)\n",
-    "a = (24.5 - mu)/sigma\n",
-    "b = (25.5 - mu)/sigma\n",
-    "(a,b)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.11246291601828484\n"
-     ]
-    }
-   ],
-   "source": [
-    "from scipy.stats import norm\n",
-    "\n",
-    "final_probability = norm.cdf(b) - norm.cdf(a)\n",
-    "print(final_probability)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "DItcIeMWnmOu",
-    "tags": []
-   },
-   "source": [
-    "# Question 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "f1k2hmfqoU47"
-   },
-   "source": [
-    "This can be represented as a joint probability mass function, where Y is the urn from which the ball is drawn. \n",
-    "\n",
-    "- $P(X=1|Y=0)=2/3$\n",
-    "- $P(X=1|Y=1)=1/6$  \n",
-    "  \n",
-    "We also know that $P(Y=1)=1/2$.  \n",
-    "This gives us all the joint probability distributions:\n",
-    "- $P(X=1, Y=1) = 1/6 * 1/2$\n",
-    "- $P(X=0, Y=1) = 5/6 * 1/2$\n",
-    "- $P(X=1, Y=0) = 2/3 * 1/2$\n",
-    "- $P(X=0, Y=0) = 1/3 * 1/2$  \n",
-    "  \n",
-    "To obtain the result 0011, we consider first the probability of drawing a 0\n",
-    "  \n",
-    "- (0)011  \n",
-    "$P(X=0) = P(X=0,Y=0) + P(X=0,Y=1)$, the marginal probability $5/6*1/2 + 1/3*1/2$.\n",
-    "\n",
-    "- 0(0)11\n",
-    "Now, for the next draw, we know we drew a 0. This indicates that we must draw from urn 0 for the next draw. This means that the next draw of 0 is $P(X=0|Y=0)=1/3$.\n",
-    "\n",
-    "- 00(1)1  \n",
-    "Following the rule, we must again draw from urn 0, but this time we must draw a 1: $P(X=1|Y=0)=2/3$\n",
-    "\n",
-    "- 001(1)  \n",
-    "We drew a 1 in the previous turn, so this final draw must be from urn 1. $P(X=1|Y=1)=1/6$\n",
-    "\n",
-    "In total, all these events must happen together, so we multiply their probabilities: $P(X=0)×P(X=0|Y=0)×P(X=1|Y=0)×P(X=1|Y=1)$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "-vEl2wTCvIQF",
-    "outputId": "09f0344c-bf6d-4abb-9c29-905fc399a06a",
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.021604938271604937"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "((5/6*1/2)+(1/3*1/2))*(1/3)*(2/3)*(1/6)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "g4Yn-ERDy9dT",
-    "tags": []
-   },
-   "source": [
-    "# Question 3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Bl2M44nqzAKz"
-   },
-   "source": [
-    "Bivariate normal distribution is given with $p(x_1, x_2)=\\mathcal{N}(\\begin{bmatrix} 0\\\\ 2 \\end{bmatrix}, \\begin{bmatrix} 0.3, -1\\\\ -1, 5 \\end{bmatrix})$, so we start by defining those constants."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "id": "v9WlFxMyy_Ax",
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "mu = np.array([0, 2])\n",
-    "sigma = np.array([[0.3, -1],[-1, 5]])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "EJivuKo0jjHi"
-   },
-   "source": [
-    "From the textbook we know that to sample a multivariate gaussian distribution, we can use the cholesky decomposition of the covariance matrix ($\\boldsymbol{\\Sigma}$) to obtain a lower left triangular matrix s.t. $\\boldsymbol{A} \\boldsymbol{A}^T=\\boldsymbol{\\Sigma}$. This allows us to obtain a sample that is multivariate normally distributed around $\\mu=0$ after finding the product of the matrix $\\boldsymbol{A}$ with a column vector of appropriate size of normally distributed gaussian random variables with mean $\\mu=0$ and variance $\\sigma^2=1$. This transforms the sample from the multinomial distribution $\\mathcal{N}(\\boldsymbol{0}, \\boldsymbol{I})$ to one in $\\mathcal{N}(\\boldsymbol{0}, \\boldsymbol{\\Sigma})$. Following that, adding the existing mean vector $\\boldsymbol{\\mu}$ shifts the means of the newly obtained distribution to the desired location, giving $\\mathcal{N}(\\boldsymbol{\\mu}, \\boldsymbol{\\Sigma})$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "id": "hS7OFA0fyF7D",
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "\n",
-    "def sample_multinomial_gaussian(mu=mu, sigma=sigma):\n",
-    "    A_mat = np.linalg.cholesky(sigma)\n",
-    "    x_vec = np.random.normal(size=2)\n",
-    "    y_vec = mu + np.dot(A_mat, x_vec)\n",
-    "    return y_vec\n",
-    "\n",
-    "samples = pd.DataFrame([sample_multinomial_gaussian() for x in range(5000)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 282
-    },
-    "id": "-3c4Q47uyIbt",
-    "outputId": "9274520d-4f85-4db5-824a-0aa9e13cfd53",
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7f46bc5d9930>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "\n",
-    "\n",
-    "plt.scatter(samples[0], samples[1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "oRz9ySAb1B4V",
-    "tags": []
-   },
-   "source": [
-    "# Question 4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {
-    "id": "ApgjWiqN1DMe",
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>Date</th>\n",
-       "      <th>Day</th>\n",
-       "      <th>High Temp (°F)</th>\n",
-       "      <th>Low Temp (°F)</th>\n",
-       "      <th>Precipitation</th>\n",
-       "      <th>Brooklyn Bridge</th>\n",
-       "      <th>Manhattan Bridge</th>\n",
-       "      <th>Williamsburg Bridge</th>\n",
-       "      <th>Queensboro Bridge</th>\n",
-       "      <th>Total</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>2016-04-01 00:00:00</td>\n",
-       "      <td>2016-04-01 00:00:00</td>\n",
-       "      <td>78.1</td>\n",
-       "      <td>66.0</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>1704.0</td>\n",
-       "      <td>3126</td>\n",
-       "      <td>4115.0</td>\n",
-       "      <td>2552.0</td>\n",
-       "      <td>11497</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2016-04-02 00:00:00</td>\n",
-       "      <td>2016-04-02 00:00:00</td>\n",
-       "      <td>55.0</td>\n",
-       "      <td>48.9</td>\n",
-       "      <td>0.15</td>\n",
-       "      <td>827.0</td>\n",
-       "      <td>1646</td>\n",
-       "      <td>2565.0</td>\n",
-       "      <td>1884.0</td>\n",
-       "      <td>6922</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>2016-04-03 00:00:00</td>\n",
-       "      <td>2016-04-03 00:00:00</td>\n",
-       "      <td>39.9</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>0.09</td>\n",
-       "      <td>526.0</td>\n",
-       "      <td>1232</td>\n",
-       "      <td>1695.0</td>\n",
-       "      <td>1306.0</td>\n",
-       "      <td>4759</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>2016-04-04 00:00:00</td>\n",
-       "      <td>2016-04-04 00:00:00</td>\n",
-       "      <td>44.1</td>\n",
-       "      <td>33.1</td>\n",
-       "      <td>0.47 (S)</td>\n",
-       "      <td>521.0</td>\n",
-       "      <td>1067</td>\n",
-       "      <td>1440.0</td>\n",
-       "      <td>1307.0</td>\n",
-       "      <td>4335</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>2016-04-05 00:00:00</td>\n",
-       "      <td>2016-04-05 00:00:00</td>\n",
-       "      <td>42.1</td>\n",
-       "      <td>26.1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1416.0</td>\n",
-       "      <td>2617</td>\n",
-       "      <td>3081.0</td>\n",
-       "      <td>2357.0</td>\n",
-       "      <td>9471</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Unnamed: 0                 Date                  Day  High Temp (°F)  \\\n",
-       "0           0  2016-04-01 00:00:00  2016-04-01 00:00:00            78.1   \n",
-       "1           1  2016-04-02 00:00:00  2016-04-02 00:00:00            55.0   \n",
-       "2           2  2016-04-03 00:00:00  2016-04-03 00:00:00            39.9   \n",
-       "3           3  2016-04-04 00:00:00  2016-04-04 00:00:00            44.1   \n",
-       "4           4  2016-04-05 00:00:00  2016-04-05 00:00:00            42.1   \n",
-       "\n",
-       "   Low Temp (°F) Precipitation  Brooklyn Bridge  Manhattan Bridge  \\\n",
-       "0           66.0          0.01           1704.0              3126   \n",
-       "1           48.9          0.15            827.0              1646   \n",
-       "2           34.0          0.09            526.0              1232   \n",
-       "3           33.1      0.47 (S)            521.0              1067   \n",
-       "4           26.1             0           1416.0              2617   \n",
-       "\n",
-       "   Williamsburg Bridge  Queensboro Bridge  Total  \n",
-       "0               4115.0             2552.0  11497  \n",
-       "1               2565.0             1884.0   6922  \n",
-       "2               1695.0             1306.0   4759  \n",
-       "3               1440.0             1307.0   4335  \n",
-       "4               3081.0             2357.0   9471  "
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "df = pd.read_csv('../data/01_raw/nyc-east-river-bicycle-counts.csv')\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Date</th>\n",
-       "      <th>Brooklyn Bridge</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2016-04-01 00:00:00</td>\n",
-       "      <td>11928.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2016-04-02 00:00:00</td>\n",
-       "      <td>5789.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2016-04-03 00:00:00</td>\n",
-       "      <td>3682.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2016-04-04 00:00:00</td>\n",
-       "      <td>3647.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2016-04-05 00:00:00</td>\n",
-       "      <td>9912.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  Date  Brooklyn Bridge\n",
-       "0  2016-04-01 00:00:00          11928.0\n",
-       "1  2016-04-02 00:00:00           5789.0\n",
-       "2  2016-04-03 00:00:00           3682.0\n",
-       "3  2016-04-04 00:00:00           3647.0\n",
-       "4  2016-04-05 00:00:00           9912.0"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chunked_dataset = df[['Date', 'Brooklyn Bridge']].groupby('Date').sum().reset_index(drop=False)\n",
-    "chunked_dataset.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "fig = sns.histplot(chunked_dataset[\"Brooklyn Bridge\"], bins=10)\n",
-    "plt.xlabel(\"Number of Bicycle Riders Per Day\")\n",
-    "plt.ylabel(\"Frequency\")\n",
-    "plt.show(fig)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For poisson distribution, seek to extract existing sample parameters and simulate them.   \n",
-    "\n",
-    "Find that the only parameter is $\\lambda$ which is the mean number of events"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2269.633333333333"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pois_lambda = df['Brooklyn Bridge'].mean()\n",
-    "pois_lambda"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2264.4666666666667"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def sample_poisson():\n",
-    "    return np.random.poisson(lam=pois_lambda)\n",
-    "\n",
-    "samples = [sample_poisson() for _ in range(chunked_dataset.shape[0])]\n",
-    "pd.Series(samples).mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig2 = sns.histplot(samples, bins=10)\n",
-    "plt.xlabel(\"Number of Bicycle Riders Per Day\")\n",
-    "plt.ylabel(\"Frequency\")\n",
-    "plt.show(fig)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Question 5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Need to simulate the behavior of M/M/1 queue and plot the number of requests that are waiting in the queue as a function of time.  \n",
-    "\n",
-    "Axes of plot are y = Number of Requests, and x = Time\n",
-    "\n",
-    "Need to simulate the queue as a function of time. Given that arrival times are independent of service times, we can obtain a sample of exponentially distributed arrival times without the need for any other information. Then, given the number of arrivals, each arrival has a service time. The number of requests at a given time interval is then given as the number of arrival, service time completion pairs that are bisected by a given time t. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "lambda1, lambda2, lambda3 = (1, 3, 4)\n",
-    "mu = 4"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import seaborn as sns\n",
-    "import pdb\n",
-    "\n",
-    "def MM1(lamb = lambda2, mu = mu, SIM_LENGTH = 2500000,  SCALING_FACTOR = 0.001) -> pd.DataFrame:\n",
-    "    queue = []\n",
-    "    log = []\n",
-    "    \n",
-    "    time_of_arrival = 0\n",
-    "    timedelta = np.random.exponential(1/lamb)\n",
-    "    \n",
-    "    # t here is a range of time [t, t+1)\n",
-    "    for t in range(SIM_LENGTH):\n",
-    "        while (SCALING_FACTOR * t) < time_of_arrival + timedelta < (SCALING_FACTOR * (t+1)):\n",
-    "            time_of_arrival += timedelta\n",
-    "            queue.append(\n",
-    "                {\n",
-    "                    \"time of arrival\": time_of_arrival,\n",
-    "                    \"time of resolution\": np.NaN\n",
-    "                }\n",
-    "            )\n",
-    "            timedelta = np.random.exponential(1/lamb)\n",
-    "            \n",
-    "        if len(queue) > 0:    \n",
-    "            if queue[0][\"time of resolution\"] is np.NaN:\n",
-    "                queue[0][\"time of resolution\"] = (SCALING_FACTOR * t) + np.random.exponential(1/mu)\n",
-    "            if (SCALING_FACTOR * t) > queue[0][\"time of resolution\"]:\n",
-    "                queue.pop(0)\n",
-    "        log.append({\"Time\": (SCALING_FACTOR * t), \"Number of Requests\": len(queue)})\n",
-    "    return pd.DataFrame(log)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## $\\lambda=1$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='Number of Requests', ylabel='Count'>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df_lam1 = MM1(lamb=lambda1, mu=mu)\n",
-    "sns.histplot(df_lam1, x=\"Number of Requests\", bins=20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.3212716"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_lam1[\"Number of Requests\"].mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.3333333333333333"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rho = lambda1/mu\n",
-    "expected_mean = rho/(1-rho)\n",
-    "expected_mean"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Good enough. Unlike the princeton demo, the code I wrote has discrete steps, which introduces a bit of variability to the "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='Time', ylabel='Number of Requests'>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# plot the number of requests that are waiting in the queue as a function of time\n",
-    "sns.scatterplot(df_lam1, y=\"Number of Requests\", x=\"Time\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## $\\lambda=2$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='Number of Requests', ylabel='Count'>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df_lam2 = MM1(lamb=lambda2, mu=mu)\n",
-    "sns.histplot(df_lam2, x=\"Number of Requests\", bins=20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.0497912"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_lam2[\"Number of Requests\"].mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.0"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rho = lambda2/mu\n",
-    "expected_mean = rho/(1-rho)\n",
-    "expected_mean"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='Time', ylabel='Number of Requests'>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#plot the number of requests that are waiting in the queue as a function of time\n",
-    "sns.scatterplot(df_lam2, y=\"Number of Requests\", x=\"Time\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## $\\lambda=3$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='Number of Requests', ylabel='Count'>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df_lam3 = MM1(lamb=lambda3, mu=mu)\n",
-    "sns.histplot(df_lam3, x=\"Number of Requests\", bins=20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "77.3641952"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_lam3[\"Number of Requests\"].mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "ename": "ZeroDivisionError",
-     "evalue": "float division by zero",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[15], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m rho \u001b[38;5;241m=\u001b[39m lambda3\u001b[38;5;241m/\u001b[39mmu\n\u001b[0;32m----> 2\u001b[0m expected_mean \u001b[38;5;241m=\u001b[39m \u001b[43mrho\u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mrho\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m expected_mean\n",
-      "\u001b[0;31mZeroDivisionError\u001b[0m: float division by zero"
-     ]
-    }
-   ],
-   "source": [
-    "rho = lambda3/mu\n",
-    "expected_mean = rho/(1-rho)\n",
-    "expected_mean"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='Time', ylabel='Number of Requests'>"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#plot the number of requests that are waiting in the queue as a function of time\n",
-    "sns.scatterplot(df_lam3, y=\"Number of Requests\", x=\"Time\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# See if we just got lucky"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " Actual: 0.142347\n",
-      "Theoretical: 0.13636363636363635\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\" Actual: {MM1(lamb=3, mu=25, SIM_LENGTH=int(2e6), SCALING_FACTOR=1e-4)['Number of Requests'].mean()}\")\n",
-    "rho = 3/25\n",
-    "expected_mean = rho/(1-rho)\n",
-    "print(f\"Theoretical: {expected_mean}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}