Skip to main content
2 votes

Comparative study between Deep neural nets and Bayesian Networks

They're not directly comparable. They do different things. They solve different problems. A Bayesian network is a probabilistic model of the relationship between multiple random variables. It is a ...
D.W.'s user avatar
  • 162k
1 vote
Accepted

Finding most likely tree over a semilattice

The maximum spanning tree is about summing weights; maximizing the probability is about multiplying weights. To convert multiplication to sums, take the logarithm. Hopefully that's enough for you to ...
D.W.'s user avatar
  • 162k
1 vote
Accepted

How do you marginalize in graphical model elimination?

I'm realizing now that it's just regular matrix multiplication. For the case above, let $p(x_5|x_3)$ and $p(\bar{x_6}|x_2,x_5)$ be represented by $r\!\times\!s$ and $s\!\times\! t$ matrices, ...
Philip Raeisghasem's user avatar
1 vote

What is the difference between "use case" and "function"?

If by "function" you mean functionality then a single use case may require more than one function to achieve and a single function may serve multiple use cases. Here's a concrete example: Use case: ...
slebetman's user avatar
  • 699
1 vote
Accepted

How static Bayesian networks are stored? Like Hugin etc

For anyone who shares this same query. I have found a link that answers it. Further, found a implementation as well.
letsBeePolite's user avatar

Only top scored, non community-wiki answers of a minimum length are eligible