title: [概率论]3-6:条件分布(Conditional Distributions Part II) categories: Mathematic Probability keywords: Multiplication Rule for Distributions 乘法法则 Bayes' Theorem 贝叶斯理论 Law of Total Probability for Random Variables 随机变量的全概率公式 toc: true date: 2018-03-12 0…

title: [概率论]3-6:条件分布(Conditional Distributions Part I) categories: Mathematic Probability keywords: Discrete Conditional Distributions 离散条件分布 Continuous Conditional Distributions 连续条件分布 toc: true date: 2018-03-08 10:38:13 Abstract: 首先介绍随机变量的条件分布,随后介绍…

title: [概率论]3-7:多变量分布(Multivariate Distributions Part II) categories: Mathematic Probability keywords: Conditional Distributions 条件分布 Bayes' Theorem 贝叶斯理论 Histograms 直方图 Law of total Probability 全概率公式 toc: true date: 2018-03-15 09:20:38 Abstract: 本文继…

title: [概率论]5-7:Gama分布(The Gamma Distributions Part II) categories: - Mathematic - Probability keywords: - The Exponential Distributions toc: true date: 2018-04-02 09:16:46 Abstract: 本文介绍Gamma分布相关知识的第二部分指数分布 Keywords: The Exponential Distributions 开篇…

title: [概率论]5-6:正态分布(The Normal Distributions Part II) categories: - Mathematic - Probability keywords: - The Normal Distributions toc: true date: 2018-03-29 15:02:03 Abstract: 本文介绍正态分布的数学性质 Keywords: The Normal Distributions 开篇废话 一共要写四篇,哪来那么多废话. 首先我…

title: [概率论]4-7:条件期望(Conditional Expectation) categories: - Mathematic - Probability keywords: - Expectation - Prediction - Law of total Probability toc: true date: 2018-03-27 10:53:24 Abstract: 本文介绍期望的条件版本,也就是条件期望 Keywords: Expectation,Prediction,La…

title: [概率论]3-2:连续分布(Continuous Distributions) categories: Mathematic Probability keywords: Continuous Random Variable 连续随机变量 Continuous Distributions 连续分布 Probability Desity Function 概率密度函数 Uniform Distributions on Intervals 均匀分布 toc: true date: 201…

title: [概率论]2-1:条件概率(Conditional Probability) categories: Mathematic Probability keywords: Conditional Probability 条件概率 Multiplication Rule 乘法原理 Partitions Law of total Probability 全概率公式 toc: true date: 2018-01-31 10:34:36 Abstract: 本文介绍条件概率的定义及相关知识,…

The results look OK, but how do you know that you aren’t missing something. Would a more sophisticated model with more variables work even better? If you add enough variables to a model, you can fit almost anything. However, you generally reach a poi…

We have seen that directed graphical models specify a factorization of the joint distribution over a set of variables into a product of local conditional distributions. They also define a set of conditional independence properties that must be satisf…