The document describes various probability distributions that can arise from combining Bernoulli random variables. It shows how a binomial distribution emerges from summing Bernoulli random variables, and how Poisson, normal, chi-squared, exponential, gamma, and inverse gamma distributions can approximate the binomial as the number of Bernoulli trials increases. Code examples in R are provided to simulate sampling from these distributions and compare the simulated distributions to their theoretical probability density functions.
The document describes various probability distributions that can arise from combining Bernoulli random variables. It shows how a binomial distribution emerges from summing Bernoulli random variables, and how Poisson, normal, chi-squared, exponential, gamma, and inverse gamma distributions can approximate the binomial as the number of Bernoulli trials increases. Code examples in R are provided to simulate sampling from these distributions and compare the simulated distributions to their theoretical probability density functions.
Tokyo Artificial Intelligence and OMOTENASHI Meetup #01 ( http://www.meetup.com/Tokyo-Artificial-Intelligence-and-OMOTENASHI-Meetup/events/175069892/ ) での amo 氏の発表のスライドを代理 Upload。
# Movie
http://www.youtube.com/playlist?list=PLvK8AB0FxScf4koa7YaxUlyNqyalBB7zD