# HMM Network Instance

An HMM network instance is a lattice-based directed conditional probability network that abides by a hidden Markov network metamodel.

**AKA:**Hidden Markov Model, Hidden Markov Graph, Viterbi Lattice.**Context:**- It can (typically) be composed of:
- It can be associated to Hidden Markov Metamodel.
- It can be a Finite-State Sequence Tagging Model.
- It can be represented with:
- It can be produced by a Hidden Markov Modeling System (that applies an HMM training algorithm to solve an HMM training task)

**Example(s):****Counter-Example(s):****See:**Undirected Probabilistic Network.

## References

### 2005

- (Cohen & Hersh, 2005) ⇒ Aaron Michael Cohen, and William R. Hersh. (2005). “A Survey of Current Work in Biomedical Text Mining.” In: Briefings in Bioinformatics 2005 6(1). doi:10.1093/bib/6.1.57
- Zhou et al. trained a
**hidden Markov model (HMM)**on a set of features based on

- Zhou et al. trained a

### 2004

- http://www.cassandra.org/pomdp/pomdp-faq.shtml
- Michael Littman's nifty explanatory grid:

Markov Models |
Do we have control over the state transitons? |
||
---|---|---|---|

NO | YES | ||

Are the states completely observable? |
YES | ## Markov Chain |
## MDPMarkov Decision Process |

NO | ## HMMHidden Markov Model |
## POMDPPartially ObservableMarkov Decision Process |

### 1997

- (Shin, Han et al., 1995) ⇒ Joong-Ho Shin, Young-Soek Han, and Key-Sun Choi. (1995). “A HMM Part-of-Speech Tagger for Korean with wordphrasal Relations". In: Proceedings of Recent Advances in Natural Language Processing (RANLP 1995)
- QUOTE: The trained hidden Markov network reflecting both morpheme and word-phrase relations contains 712 nodes and 28553 edges.

### 1993

- (Tanaka et al., 1993) ⇒ H. Tanaka & al. (1993). “Classification of Proteins via Successive State Splitting of Hidden Markov Network.” In: ProceedingsW26 in IJCAI93,