{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 8 Hidden Markov Models\n", "\n", "## 8.1 The Markov Chain( Discrete-time Markov chain)\n", "\n", "Let \\\\(\\mathbf{X}=X_1X_2...X_n\\\\) be a sequence of random variables from a finite dicrete alphabat \\\\(\\mathbf{O}=o_1o_2...o_M\\\\), then\n", "\n", "$$ P(X_1,X_2,...,X_n)=P(X_1)\\prod_{i=2}^{n}P(X_i|X_1^{i-1}) \\tag{8.1}$$\n", "\n", "where \\\\(X_1^{i-1}=X_1X_2...X_{i-1}\\\\).\n", "\n", "* **first-order Markov chain(first-order Markov assumption):**\n", "\n", "$$P(X_i|X_1^{i-1})=P(X_i|X_{i-1}) \\tag{8.2}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Building ..." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }