Course talk:CPSC522/Markov Networks

Hi Bahare,

Sorry for the late notice.  :( I've dealt with most of the critiquers' suggestions. It seems the most significant remaining suggestion is the addition of content about applications of Markov networks. I've listed some of the applications with links to relevant Wikipedia pages at the beginning of the Content section; but it may be a good idea to have an example or more detailed description of one of them in a new section at the end. I doubt I'll have time to do that (due to other constraints), and so if you want to put something along those lines into the page, you're more than welcome to do so.

If you don't have time either, that's okay; I think the page is satisfactory as-is, if not exemplary.

Clear skies, Jordon

I don't know why I just get your thread!!! It seems like the page was not refreshed and I did not get any notification about any thread in discussion parts. I'm so sorry Jordan :(

No worries, I've noticed issues with the discussion threads as well. Life goes on, and we will survive.  :)

The page is great, easy to understand. But I think it would be better to reorganize the "Probabilities and Factors" parts, maybe introduce the example first, but doesn't give the solution, and then introduce the theory, how to calculate the $P(x)$, $Z$, and finally explain how to use the value or given number in the example to solve those problem, that would be easy for people to understand.

Thanks for the feedback! I get what you mean about the Probabilities and Factors section; but I'm not sure a full reorganization is necessary. I've put in links to make sure people know about the worked example, and I've added some text to more clearly motivate that the purpose of the math is to allow us to calculate probabilities; we'll see during the grading if those measures are adequate.

Hi Jordon Johnson,

Excellent work. I am really impressed by your page and like the pictures you present very much. However, this page seems a little short. You can extend your page by adding more content including the introduction, applications of Markov Networks, etc.

Sincerely, Ke Dai

Hi Jordon,

It is nice to read your page, I like pictures you gave to describe Markov Networks and its properties. I only have a few suggestions for you to think about:

1. Variables and definitions like G = (V, E) should better in italic.

2. The citation is not in the correct form, the location it appears in your page should be indicated.

3. I think if you provide some applications of Markov networks will help me to better understand it.

Best regards,

Jiahong Chen

Your draft is so perfect that now I know more clearly about Markov Networks. Here is my critiques and if you have questions let me know. Scale of 1 to 5, where 1 = strongly disagree and 5 = strongly agree:

• (4) The topic is relevant for the course.
• (5) The writing is clear and the English is good.
• (5) The page is written at an appropriate level for CPSC 522 students (where the students have diverse backgrounds).
• (5) The formalism (definitions, mathematics) was well chosen to make the page easier to understand.
• (4.5) The abstract is a concise and clear summary.
• (5) There were appropriate (original) examples that helped make the topic clear.
• (3) There was appropriate use of (pseudo-) code.
• (4) It had a good coverage of representations, semantics, inference and learning (as appropriate for the topic).
• (5) It is correct.
• (4) It was neither too short nor too long for the topic.
• (4) It was an appropriate unit for a page (it shouldn't be split into different topics or merged with another page).
• (4.5) It links to appropriate other pages in the wiki.
• (4) The references and links to external pages are well chosen.
• (4.5) I would recommend this page to someone who wanted to find out about the topic.
• (4) This page should be highlighted as an exemplary page for others to emulate.

Here are some suggestions:

• 1 Abstract section can be more concise.
• 2 It is better to introduce some Artificial intelligence or machine learning areas that use Markov network. For example: The reason why Artificial intelligence uses directed graph like Bayesian network more compared with Markov network. What Markov network do in the domain of artificial intelligence and so on.

Sincerely,

YuYan