I am just getting started on understanding Factor Graphs, by going through the excellent guide Factor Graph For Robot Perception by Dallaert and Kaess

While I am familiar with the SLAM problem, and have a fair understanding of the probability and statistics involved, I fail to understand why a Factor Graph is a better representation compared to a Bayes net.

To me, they seem to be the same thing. In the Bayes Net, you visualize functions (factors) by incoming and outgoing edges, while in a factor graph, you represent it by a bridge (node) between nodes. However, the underlying principle is the same, i.e. MAP and MLE, yielding the same computation.

Thus, fundamentally, what is the difference between a Bayes Net and a Factor Graph. (I understand what they are, but cannot really seem to point out the difference between them). And consequently, in what way does a Bayes Net fail to capture the SLAM problem, that a Factor Graph can?

Any explanation with examples would be appreciated!

  • 1
    $\begingroup$ I think this is a good question, but I had to edit the title to make it not opinion based. Questions that ask about the pros/cons or advantages/disadvantages are effectively opinion polls, which are discouraged on stack exchange. We prefer practical, answerable questions based on actual problems that you face. $\endgroup$
    – Ben
    Jan 31 at 14:51
  • $\begingroup$ Let me reiterate that I think asking the difference between a Bayes Net and a Factor Graph is valid and shout stay open. $\endgroup$
    – Ben
    Jan 31 at 14:52

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