Probability Chain, inference data analysis, machine learning

2019-2020 tavasz

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Téma leírása

Probability Chain, inference data analysis

Introduction

A common quotation in the statistics book that ironically criticizes Bayesian analysis; "The Bayesian is the one who vaguely expects a horse and catches the glimpse of a donkey, strongly believes he has seen a mule." The following is a major issue: a. Another quote about Frequentist analysis said; "A frequentist is a person whose long-run ambition is to be wrong 5% of the time." The objective is to find out what the data are, and what the objective is. However, some statistical applications are suggested starting with a clever pick of the prior. An "uninformative" or "diffuse" prior can be found in the results. In other words, uninformative priors express "objective variable" or "The simplest rule for determining non-informative priority is the assignment epistemic probabilities. Suppose that there are n> 1 mutually exclusive and collectively exhaustive possibilities.

https://www.youtube.com/watch?v=r76oDIvwETI

https://www.youtube.com/watch?v=KhAUfqhLakw

http://jakevdp.github.io/blog/2014/03/11/frequentism-and-bayesianism-a-practical-intro/

Course objective:

Quotes from: http://www.worldworld.org/\tPage 7

Feltételek

  • gépi tanulás, machine learning, deep learning, GAN,

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