between tags and words, there‘s table 1.
between tags, there‘s table 2.
combine the two tables, p(...) to get the results.
MRF: factors of the tables not necessarily probabilities
BN: must be probabilities. => BN is easier to learn than MRF
Maximum-Entropy Markov Model (MEMM)
Marginals:
1) forward:
2) Belief:
HMM is generative, modeling joint probability P(x,y)
but tagging just needs P(y|x)
https://cedar.buffalo.edu/~srihari/CSE574/Discriminative-Generative.pdf
Full obervation!
(like the offline SLAM?)
biased! because we only look at local observation.
P（x_2|x_1） can be called \Psi(x_1,x_2)
If Y_1 ~~~~ Y_{n-2} are connected somehow, what should be changed?
How close the model is closed to the truth.
2018 10-708 (CMU) Probabilistic Graphical Models {Lecture 10} [HHM and CRF]
原文：https://www.cnblogs.com/ecoflex/p/10231319.html