Understanding Markov Chain by Comparing “First Order Sequence Model” and “Second Order Sequence Model”

Paul Xiong
Apr 27, 2023

First, let’s assuming we have a book, count follow sentences in a book:

  • Check whether the battery ran down please.
  • Check whether the program ran please.

Totally, they appeared for 100 times; the first line appeared for 40 times and second for 60 times.

Our goal here is to predict next word after word “ran”.

  • First Order Sequence: only look at “ran” , so next word could be “down” (.4 probability) and “please”(.6 probability).
pic shows 3 steps(1,2,3) to look at how to find the corresponding probabilities.
  • Second Order Sequence: look at two words, “ran down” + “please” (1 probability) and “program ran” + “please” (1 probability). Comparing with above .4 and .6, the predicting rate is improved.



Paul Xiong

Coding, implementing, optimizing ML annotation with self-supervised learning, TLDR: doctor’s labeling is the 1st priority for our Cervical AI project.