Discussion
How does a Bayesian network arise from the chain rule?
Answer: A
The chain rule always gives a factorisation, and conditional independence lets each factor drop the predecessors that add nothing, leaving only the parents. The saving comes entirely from those dropped conditionings. Exactness is unaffected by the ordering, repetition is not required and estimation is a separate matter. Deriving the representation from an identity plus an assumption, rather than positing it, shows exactly where its power comes from.
No comment is present. Be the first to comment.
Loading…
Post your comment
Please log in or register to post a comment.
Examination Categories
- SSC Exams
- Banking Exams
- Teaching Exams
- Civil Services Exams
- Railways Exams
- Engineering Recruitment Exams
- Defence Exams
- State Government Exams
- Police Exams
- Insurance Exams
- Nursing Exams
- Other Government Exams
- NRA CET
- PG Entrance Exams
- Campus Placement
- MBA Entrance Exams
- CUET and UG Entrance
- Judiciary Exams
- Regulatory Body Exams
- Software Development