From 9ec15598b0f6cd975875f04ce30fad37f38c2809 Mon Sep 17 00:00:00 2001 From: TheMaskulladore4321 Date: Mon, 7 Jul 2025 21:32:47 +0530 Subject: [PATCH 1/3] Add files via upload --- ProbStats1.ipynb | 244 +++++++++++++++++++++++++++++++++++++++++++++++ ProbStats2.ipynb | 170 +++++++++++++++++++++++++++++++++ 2 files changed, 414 insertions(+) create mode 100644 ProbStats1.ipynb create mode 100644 ProbStats2.ipynb diff --git a/ProbStats1.ipynb b/ProbStats1.ipynb new file mode 100644 index 0000000..8338d43 --- /dev/null +++ b/ProbStats1.ipynb @@ -0,0 +1,244 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "g7QBdPyjAiRr" + }, + "source": [ + "#Q1\n", + "\n", + "$\n", + "Y = |Z| \\\\\n", + "Z \\sim Normal(0, 1) \\\\\n", + "\\implies f_Z (z) = \\dfrac{1}{\\sqrt{2\\pi}} e^ {-z^2/2 } \\\\\n", + "\\implies F_Y (y) = P(|Z| \\leq y) = P(-y \\leq Z \\leq y) \\\\\n", + "\\qquad = F_Z(y) - F_Z(-y) \\\\\n", + "Differentiating \\; both \\; sides,\\\\\n", + "f_Y(y) = 2 f_Z(y) = \\sqrt{\\dfrac{2}{\\pi}} e^ {-y^2/2 } \\\\\n", + "E(Y) = \\int_0^\\infty y \\sqrt{\\dfrac{2}{\\pi}} e^ {-y^2/2 } \\: \\mathrm{d}y \\\\\n", + "\\qquad = \\sqrt{\\dfrac{2}{\\pi}} \\\\\n", + "E(Y^2) = E(Z^2) = {\\sigma_Z^2} + {\\mu_Z^2} = 1 \\\\\n", + "E(Y^3) = \\int_0^\\infty y^3 \\sqrt{\\dfrac{2}{\\pi}} e^ {-y^2/2 } \\: \\mathrm{d}y \\\\\n", + "\\qquad = \\int_0^\\infty 2t \\sqrt{\\dfrac{2}{\\pi}} e^{-t} \\: \\mathrm{d}t \\\\\n", + "\\qquad = 2\\sqrt{\\dfrac{2}{\\pi}}\n", + "$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XBcMk5e7OFy6" + }, + "source": [ + "#Q2\n", + "\n", + "\n", + "The LoTUS states:\n", + "\n", + "$\n", + "E(g(X)) = \\sum g(x)f_X(x) \\\\\n", + "Given \\: that \\: X \\sim Poisson(\\lambda) \\\\\n", + "\\implies f_X(x) = e^{-\\lambda}{\\lambda}^x/x! \\\\\n", + "To \\: prove \\: that \\: E(Xg(X)) = \\lambda E(g(X + 1)) \\\\\n", + "= \\sum_{x=0}^{∞} xg(x) \\dfrac{e^{-\\lambda}{\\lambda}^x}{x!} \\\\\n", + "= \\lambda\\sum_{x=0}^{∞} g(x) \\dfrac{e^{-\\lambda}{\\lambda}^{x - 1}}{(x - 1)!} \\\\\n", + "= \\lambda\\sum_{t=-1}^{∞} g(t + 1) \\dfrac{e^{-\\lambda}{\\lambda}^t}{t!} \\\\\n", + "= \\lambda\\sum_{t=0}^{∞} g(t + 1) \\dfrac{e^{-\\lambda}{\\lambda}^t}{t!} \\\\\n", + "\\qquad (as \\: (-1)! \\to -∞, the \\; t = -1 \\: term \\: is \\: 0.) \\\\\n", + "= \\lambda E(g(X+1))\\\\\n", + "LHS = RHS, \\; hence \\; proven.\n", + "$\n", + "\n", + "---\n", + "\n", + "$\n", + "E(X^3) = E(XX^2) = λE(X^2 + 2X + 1) \\\\\n", + "\\tiny{Using \\: (E(X^2) = E(XX) = \\lambda E(X + 1) = \\lambda E(X) + \\lambda)} \\\\\n", + "\\qquad = {\\lambda}^3 + 4\\lambda + \\lambda \\\\\n", + "$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pbq20qiicdrr" + }, + "source": [ + "#Q3\n", + "\n", + "$\n", + "T_1 \\sim Exp(\\lambda_1) \\\\\n", + "T_2 \\sim Exp(\\lambda_2) \\\\\n", + "P(T_1 < T_2) = \\int_{0}^{∞} \\int_{t_1}^{∞} f_{T_1}(t_1)f_{T_2}(t_2) \\; \\mathrm d{t_2}d{t_1} \\\\\n", + "= \\lambda_1 \\lambda_2 \\int_{0}^{∞} \\int_{t_1}^{∞} e^{-\\lambda_1 t_1 -\\lambda_2 t_2} \\; \\mathrm d{t_2}d{t_1} \\\\\n", + "= -\\lambda_1 \\int_{0}^{∞} e^{-({\\lambda_1 +\\lambda_2})t_1} \\; \\mathrm d{t_1} \\\\\n", + "= \\dfrac{\\lambda_1}{\\lambda_1 + \\lambda_2}\n", + "$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jZjLFHN2lX3A" + }, + "source": [ + "#Q4\n", + "\n", + "Assuming that one message is just one bit; either 0 or 1, with each having equal likelihood.\n", + "\n", + "Then, the sender's bit has 0.5 chance of being 0 or 1, to which we add $X \\sim Normal(0, \\sigma^2)$. If after this, the result is >0.5, it is interpreted by the reciever as 1, else 0.\n", + "\n", + "$\n", + "P(Correct \\; Interpretation) = P(S=0)P(\\dfrac{R=0}{S=0}) + P(S=1)P(\\dfrac{R=1}{S=1}) \\\\\n", + "= \\dfrac{1}{2}[F_X(\\dfrac{1}{2}) + 1 - F_X(-\\dfrac{1}{2})] \\\\\n", + "=\\Phi(\\dfrac{1}{2\\sigma})\n", + "$\n", + "\n", + "$\\implies$ When $\\sigma \\to 0$, $\\Phi(\\dfrac{1}{2\\sigma}) \\to \\Phi(∞) = 1$ which makes sense as a variance of 0 implies no random change at all, and since the mean is already 0, the bit remains unchanged in all scenarios." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cE-mzFNPw6RG" + }, + "source": [ + "#Q5\n", + "\n", + "- Four distributions, Exponential, Binomial, Poisson and a mixed Normal-Uniform.\n", + "- We generate 1000 arrays of random values of size 80 for each distribution and a 1000 more, with size 30.\n", + "- Then we find their means.\n", + "- According to the CLT, as the number of samples increases, their distribution should tend towards a normal distribution.\n", + "- To check we plot them and and check the difference between the sample mean and population mean (and std. dev.)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "qiG-EZhIpn6p" + }, + "outputs": [], + "source": [ + "#Total 8 arrays.\n", + "import numpy as np\n", + "\n", + "observed_number = [80, 30]\n", + "exp_means = np.zeros((2, 1000))\n", + "binom_means = np.zeros((2, 1000))\n", + "pois_means = np.zeros((2, 1000))\n", + "mix_means = np.zeros((2, 1000))\n", + "\n", + "for i in range(2):\n", + " for j in range(1000):\n", + " exp_means[i][j] = (sum(np.random.default_rng().exponential(10, observed_number[i])) / observed_number[i])\n", + " binom_means[i][j] = (sum(np.random.default_rng().binomial(100, 0.7, observed_number[i])) / observed_number[i])\n", + " pois_means[i][j] = (sum(np.random.default_rng().poisson(10, observed_number[i])) / observed_number[i])\n", + " mix_means[i][j] = np.random.default_rng().choice([(sum(np.random.default_rng().normal(50, 10, observed_number[i])) / observed_number[i]), (sum(np.random.default_rng().uniform(20, 80, observed_number[i])) / observed_number[i])])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 720 + }, + "id": "biRy-69L01iM", + "outputId": "9834831a-42e9-463a-f23a-ca15fc40cb95" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "titles = [\"exponential set of 80\", \"exponential set of 30\",\n", + " \"binomial set of 80\", \"binomial set of 30\",\n", + " \"poisson set of 80\", \"poisson set of 30\",\n", + " \"mixed uniform-normal set of 80\", \"mixed uniform-normal set of 30\"]\n", + "\n", + "sets = [exp_means[0], exp_means[1],\n", + " binom_means[0], binom_means[1],\n", + " pois_means[0], pois_means[1],\n", + " mix_means[0], mix_means[1]]\n", + "\n", + "fig, axes = plt.subplots(4, 2, figsize = (16, 8))\n", + "axes = axes.flatten()\n", + "\n", + "for i, ax in enumerate(axes):\n", + " sns.histplot(sets[i], bins = 100, color='blue', edgecolor='black', kde = True, ax = ax)\n", + " ax.set_title(titles[i])\n", + " ax.set_xlabel(\"Sample Means\")\n", + " ax.set_ylabel(\"Frequency\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2sIzYdR9Gjth", + "outputId": "34d06052-7077-4f72-d6c4-a44ddcdfc6de" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-2.8284271247461903\n", + "0.004677734981047177\n", + "Lifetime is not 800 hours\n" + ] + } + ], + "source": [ + "from statistics import NormalDist\n", + "alpha = 0.05\n", + "sample_mean = 780\n", + "sample_std_dev = 50\n", + "n = 50\n", + "\n", + "# Hypothesis is that the lifetime is above 800 hours, ie the true/population mean is 800 hours\n", + "\n", + "z = (sample_mean - 800) / (sample_std_dev / np.sqrt(n))\n", + "print(z)\n", + "# Two tailed test since lifetime is either =800 or simply not. So we calculate P(|Z| > |z| | H_0) = p.\n", + "p = 2*(1 -NormalDist(mu = 0, sigma = 1).cdf(abs(z)))\n", + "print(p)\n", + "print(\"Lifetime is not 800 hours\") if p < alpha else print(\"Lifetime is 800 hours\")" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ProbStats2.ipynb b/ProbStats2.ipynb new file mode 100644 index 0000000..0d7a5d4 --- /dev/null +++ b/ProbStats2.ipynb @@ -0,0 +1,170 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "49cee8ee", + "metadata": {}, + "source": [ + "#Q1\n", + "\n", + "Bayes' Formula for upadation of probability: \n", + "$$\n", + "P(H|D) = \\dfrac{P(D|H)P(H)}{P(D)}\n", + "$$\n", + "\n", + "The Prior: P(Expert) = 0.01\n", + "Likelihoods: P(3 Bullseyes in 5 throws | Expert) can be computed as $(P(Bullseye | Expert))^3$ = $(0.7)^3(0.3)^2$, since each throw is independent. If he's not an expert, it's $(0.1)^3(0.9)^2$.\n", + "\n", + "Hence, we use the Bayesian Update,\n", + "\n", + "$$\n", + "P(Expert|3\\;Bullseyes\\;in\\;5\\;throws) = \\dfrac{P(3\\;Bullseyes\\;in\\;5\\;throws|Expert)P(Expert)}{P(3\\;Bullseyes\\;in\\;5\\;throws)}\\\\\n", + "= \\dfrac{(0.7)^3\\times(0.3)^2\\times(0.01)}{(0.1)^3\\times(0.9)^2\\times0.99 + (0.7)^3\\times(0.3)^2\n", + "\\times0.01}\\\\\n", + "\\approx 0.27795\n", + "$$\n", + "\n", + "The probability goes from 1% to $\\approx$ 28% based on his performance being way better than what would be expected of an average person.\n", + "If our prior was 20% instead of 1%, our posterior would grow to 0.9050 or $\\approx$ 90.5% since the prior data informs the posterior. Our higher belief in the original hypothesis increases our probability of it being true. " + ] + }, + { + "cell_type": "markdown", + "id": "78e06a3d", + "metadata": {}, + "source": [ + "#Q2\n", + "\n", + "Our set of times: ${T_1, T_2, ..., T_n}$\n", + "\n", + "Given that $T_i > 10$ and $T \\sim Exp(\\lambda)$.\n", + "\n", + "Then we must change the stnadard exponential PDF such that $\\int_{10}^{\\infty} k \\times f_T(t) \\: \\mathrm{d}t = 1$.
\n", + "This gives us $g_T(t) = \\dfrac{f_X(x)}{F(\\infty) - F(10)} = \\lambda {\\mathrm{e}}^{-\\lambda(t - 10)}\\\\$\n", + "$$\n", + "L(\\lambda) = {\\lambda}^n \\prod_{i=1}^{n} {\\mathrm{e}}^{-\\lambda(T_i - 10)}\\\\\n", + "l(\\lambda) = \\log (L(\\lambda)) = n\\log (\\lambda) - \\lambda \\sum_{i = 1}^{n} (T_i - 10)\\\\\n", + "$$\n", + "Differentiating this wrt $\\lambda$,\n", + "$$\n", + "\\dfrac{n}{\\lambda} - \\sum_{i = 1}^{n} (T_i - 10) = 0\\\\\n", + "\\hat {\\lambda} = \\dfrac{n}{\\sum_{i = 1}^{n} (T_i - 10)}\\\\\n", + "$$\n", + "\n", + "Ignoring truncation would ofcourse give an incorrect pdf, which would represent the distribution of data not as it happened. Our model would then have a region of the distribution without any observations, and this would take away from the $T_i > 10$ region, giving waiting times less than they're supposed to be.\n", + "\n", + "If the device had a little bit of uncertainty that makes it sometimes start later than 10 minutes, we could model that uncertainty to get better predictions, probably as part of a mixed distribution of the waiting time where the point of truncation is variable.\n", + "\n", + "\n", + "Now if we give $\\lambda$ a $\\gamma (a, b)$ prior, the following changes:\n", + "\n", + "$\\hat \\lambda$ is $\\lambda$ that maximises $f_{Data|\\lambda}(data|t)f_{\\lambda}(t)$, where the former is the likelihood and the latter is the prior.\n", + "\n", + "The MLE only maximises the likelihood. We can use the likelihood function from our previous calculation.\n", + "\n", + "$$\n", + "f_{Data|\\lambda}(data|t)f_{\\lambda}(t) \\propto {\\lambda}^{n + \\alpha - 1}{{\\mathrm{e}}^{({\\dfrac{-\\lambda}{\\beta}})}}{\\prod_{i=1}^{n} {\\mathrm{e}}^{-\\lambda(T_i - 10)}}\n", + "$$\n", + "Taking a logarithm and differentiating, then equating to zero gives us the value:\n", + "\n", + "$\\hat \\lambda_{MAP} = \\dfrac {n + \\alpha - 1}{\\dfrac{1}{\\beta} + \\sum_{i = 1}^{n} (T_i - 10)}$\n", + "\n", + "The difference between $\\hat \\lambda_{MAP}$ and $\\hat \\lambda_{MLE}$ is $\\propto \\alpha,\\;\\beta$.\n", + "This is higher when the historical data is *very* different from the current model.\n", + "\n", + "The prior acts as said historical data, it pulls MAP towards itself, especially if data is scarce.\n", + "We should prefer MAP over MLE when in the data collected so far is low." + ] + }, + { + "cell_type": "markdown", + "id": "b0a360a0", + "metadata": {}, + "source": [ + "#Q3\n", + "\n", + "$$\n", + "D_{{KL}}(P \\parallel Q) = \\sum_{i=1}^k p_i \\log \\left( \\frac{p_i}{q_i} \\right)\n", + "$$\n", + "\n", + "We want to show that:\n", + "\n", + "$$\n", + "D_{{KL}}(P \\parallel Q) \\geq 0\n", + "$$\n", + "\n", + "The logarithm function is strictly concave. By Jensen's inequality:\n", + "\n", + "$$\n", + "\\sum_{i=1}^k p_i \\log \\left( \\frac{q_i}{p_i} \\right) \\leq \\log \\left( \\sum_{i=1}^k p_i \\cdot \\frac{q_i}{p_i} \\right) = \\log \\left( \\sum_{i=1}^k q_i \\right) = \\log(1) = 0\n", + "$$\n", + "\n", + "Multiplying both sides by \\(-1\\), we obtain:\n", + "\n", + "$$\n", + "\\sum_{i=1}^k p_i \\log \\left( \\frac{p_i}{q_i} \\right) \\geq 0\n", + "$$\n", + "\n", + "Thus,\n", + "\n", + "$$\n", + "D_{{KL}}(P \\parallel Q) \\geq 0\n", + "$$\n", + "\n", + "\n", + "\n", + "When is \\( D_{\\text{KL}}(P \\parallel Q) = 0 \\) ?\n", + "\n", + "This occurs if and only if:\n", + "\n", + "$$\n", + "p_i = q_i \\quad \\text{for all } i\n", + "$$\n", + "\n", + "That is, \\( P = Q \\). This follows from the strict convexity of the KL divergence.\n", + "\n", + "\n", + "\n", + "Connection to Cross-Entropy\n", + "\n", + "The cross-entropy between distributions \\( P \\) and \\( Q \\) is defined as:\n", + "\n", + "$$\n", + "H(P, Q) = - \\sum_{i=1}^k p_i \\log(q_i)\n", + "$$\n", + "\n", + "The entropy of \\( P \\) is:\n", + "\n", + "$$\n", + "H(P) = - \\sum_{i=1}^k p_i \\log(p_i)\n", + "$$\n", + "\n", + "We can express the KL divergence in terms of entropy and cross-entropy:\n", + "\n", + "$$\n", + "D_{\\text{KL}}(P \\parallel Q) = \\sum_{i=1}^k p_i \\log \\left( \\frac{p_i}{q_i} \\right) = \\sum_{i=1}^k p_i \\log(p_i) - \\sum_{i=1}^k p_i \\log(q_i)\n", + "$$\n", + "\n", + "$$\n", + "D_{\\text{KL}}(P \\parallel Q) = -H(P) + H(P, Q)\n", + "$$\n", + "\n", + "\n", + "Minimizing \\( D_{\\text{KL}}(P \\parallel Q) \\) with respect to \\( Q \\) is equivalent to minimizing the cross-entropy \\( H(P, Q) \\), up to the constant \\( H(P) \\), which depends only on \\( P \\)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.13.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 4c969284bf27fe2c1c73e4feb90c7f09c26b606b Mon Sep 17 00:00:00 2001 From: TheMaskulladore4321 Date: Mon, 7 Jul 2025 21:35:48 +0530 Subject: [PATCH 2/3] Rename ProbStats2.ipynb to Tanmay/ProbStats2.ipynb --- ProbStats2.ipynb => Tanmay/ProbStats2.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename ProbStats2.ipynb => Tanmay/ProbStats2.ipynb (100%) diff --git a/ProbStats2.ipynb b/Tanmay/ProbStats2.ipynb similarity index 100% rename from ProbStats2.ipynb rename to Tanmay/ProbStats2.ipynb From dd3c1627a6a659c1746cbd23ff77d10083b5fabd Mon Sep 17 00:00:00 2001 From: TheMaskulladore4321 Date: Mon, 7 Jul 2025 21:36:20 +0530 Subject: [PATCH 3/3] Rename ProbStats1.ipynb to Tanmay/ProbStats1.ipynb --- ProbStats1.ipynb => Tanmay/ProbStats1.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename ProbStats1.ipynb => Tanmay/ProbStats1.ipynb (100%) diff --git a/ProbStats1.ipynb b/Tanmay/ProbStats1.ipynb similarity index 100% rename from ProbStats1.ipynb rename to Tanmay/ProbStats1.ipynb