摘要:Tensorflow Distributions提供了两类抽象:distributions和bijectors.distributions提供了一系列具备快速.数值稳定的采样.对数概率计算以及其他统计特征计算方法的概率分布.bijectors提供了一系列针对distribution的可组合的确定性变换. 1.Distributions 1.1 methods 一个distribution至少实现以下方法:sample.log_prob.batch_shape_tensor.event_sh…

2.1. Binary Variables 1. Bernoulli distribution, p(x = 1|µ) = µ 2.Binomial distribution + 3.beta distribution(Conjugate Prior of Bernoulli distribution) The parameters a and b are often called hyperparameters because they control the distribution of…

主讲人 网络上的尼采 (新浪微博: @Nietzsche_复杂网络机器学习) 网络上的尼采(813394698) 9:11:56 开始吧,先不要发言了,先讲PRML第二章Probability Distributions.今天的内容比较多,还是边思考边打字,会比较慢,大家不要着急,上午讲不完下午会接着讲. 顾名思义,PRML第二章Probability Distributions的主要内容有:伯努利分布. 二项式 –beta共轭分布.多项式分布 -狄利克雷共轭分布 .高斯分布 .频率派和贝叶斯派…

PRML Chapter 2. Probability Distributions P68 conjugate priors In Bayesian probability theory, if the posterior distributions p(θ|x) are in the same family as the prior probability distributionp(θ), the prior and posterior are then called conjugate d…

Common Probability Distributions Probability Distribution A probability distribution describes the probabilities of all the possible outcomes for a random variable. A discrete random variable if one for which the number of possible outcomes can be co…

Basics of Probability Probability density function (pdf). Let X be a continuous random variable. Then a probability distribution or probability density function (pdf) of X is a function f(x) such that any two numbers a and b with That is, the probabi…

PDF version PDF & CDF The probability density function is $$f(x; \mu, \sigma) = {1\over\sqrt{2\pi}\sigma}e^{-{1\over2}{(x-\mu)^2\over\sigma^2}}$$ The cumulative distribution function is defined by $$F(x; \mu, \sigma) = \Phi\left({x-\mu\over\sigma}\ri…

PDF version PDF & CDF The probability density function of the uniform distribution is $$f(x; \alpha, \beta) = \begin{cases}{1\over\beta-\alpha} & \mbox{if}\ \alpha < x < \beta\\ 0 & \mbox{otherwise} \end{cases} $$ The cumulative distribu…

PDF version PDF & CDF The exponential probability density function (PDF) is $$f(x; \lambda) = \begin{cases}\lambda e^{-\lambda x} & x\geq0\\ 0 & x < 0 \end{cases}$$ The exponential cumulative distribution function (CDF) is $$F(x; \lambda) =…

PDF version PMF Suppose that a sample of size $n$ is to be chosen randomly (without replacement) from an urn containing $N$ balls, of which $m$ are white and $N-m$ are black. If we let $X$ denote the number of white balls selected, then $$f(x; N, m,…