What Is Bayesian Inference?. From intuitive explanation to

 By Medium - 2021-03-19

Description

This post is a part of a series of posts that I will be making. You can read a more detailed version of this post on my personal blog by clicking here. Underneath you can see an overview of the…

Summary

  • From intuitive explanation to mathematical theory with a classic coin toss example Contents This post is a part of a series of posts that I will be making.
  • what the data says versus what we know from the data.
  • We therefore want to find the value of θ that maximizes Note that (3) expresses the likelihood of θ given D, which is not the same as saying the probability of θ given D. The image underneath shows our likelihood function P(D∣θ) (as a function of θ) and the maximum likelihood estimate.
  • Since it’s a beta distribution, we can look at (4) and see that it must be Like earlier, we’ll also assume that the coin tosses are independent, which means that the probability of seeing 2 heads in a row (given θ and the data D) is just equal to the probability of seeing heads squared, i.e, P(H∣θ, D)=θ².

 

Topics

  1. Machine_Learning (0.21)
  2. Backend (0.14)
  3. NLP (0.11)

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