<p>CRF is for HMM in sequences classification as Logistic Regression is for Naive Bayes for simple classification. For a in depth difference between them I strongly suggest you to read the classical Sutton article <a href="https://homepages.inf.ed.ac.uk/csutton/publications/crftut-fnt.pdf" rel="nofollow noreferrer">"An Introduction to Conditional
Random Fields"</a>. For a more practical example explaining how to apply the math I suggest you to read this <a href="https://towardsdatascience.com/conditional-random-field-tutorial-in-pytorch-ca0d04499463" rel="nofollow noreferrer">article</a>.</p>

CC BY-SA 4.0
2018-12-15T00:52:59.227Z
2018-12-15T00:52:59.227Z
xboard
<p>I am learning about probabilistic graphical models and I was wondering if there is an example explaining the math behind <em>conditional random fields</em>. Looking solely on the formula, I have no idea what we actually do. I found a lot of examples for the <em>hidden Markov model</em>. There is a part speech-tag task where we have to find the tags for the sentence "flies like a flower". On these slides (slide 8) <a href="http://www.cse.aucegypt.edu/~rafea/CSCE569/slides/ch7.pdf" rel="nofollow noreferrer">Ambiguity Resolution: Statistical Method-Prof. Ahmed Rafea</a>, an HMM is used to find the correct tags. How would I transform this model into a CRF and how would I apply the math?</p>

<math><reference-request><generative-model><conditional-random-field>
Is there a mathematical example for Conditional Random Fields?
xava
# Is there a mathematical example for Conditional Random Fields?

(Asked by: username_0 on 2018-12-13)

I am learning about probabilistic graphical models and I was wondering if there is an example explaining the math behind *conditional random fields*. Looking solely on the formula, I have no idea what we actually do. I found a lot of examples for the *hidden Markov model*. There is a part speech-tag task where we have to find the tags for the sentence "flies like a flower". On these slides (slide 8) [Ambiguity Resolution: Statistical Method-Prof. Ahmed Rafea](http://www.cse.aucegypt.edu/~rafea/CSCE569/slides/ch7.pdf), an HMM is used to find the correct tags. How would I transform this model into a CRF and how would I apply the math?

**Tags:** math, reference-request, generative-model, conditional-random-field


---

## 1 answers

### Answer by username_1 on 2018-12-15. Score: 1

CRF is for HMM in sequences classification as Logistic Regression is for Naive Bayes for simple classification. For a in depth difference between them I strongly suggest you to read the classical Sutton article ["An Introduction to Conditional
Random Fields"](https://homepages.inf.ed.ac.uk/csutton/publications/crftut-fnt.pdf). For a more practical example explaining how to apply the math I suggest you to read this [article](https://towardsdatascience.com/conditional-random-field-tutorial-in-pytorch-ca0d04499463).


---

