Name
Abstract | ||
|---|---|---|
Conditional Markov Chains (also known as Linear-Chain Conditional Random Fields in the literature) are a versatile class of discriminative models for the distribution of a sequence of hidden states conditional on a sequence of observable variables. Large-sample properties of Conditional Markov Chains have been first studied by Sinn and Poupart [1]. The paper extends this work in two directions: first, mixing properties of models with unbounded feature functions are being established; second, necessary conditions for model identifiability and the uniqueness of maximum likelihood estimates are being given. |
Year | Venue | Field |
|---|---|---|
2012 | NIPS | Applied mathematics,Discrete mathematics,Mathematical optimization,Conditional variance,Maximum-entropy Markov model,Conditional probability distribution,Markov chain,Regular conditional probability,Variable-order Markov model,Hidden Markov model,Mathematics,Examples of Markov chains |
DocType | Citations | PageRank |
Conference | 1 | 0.40 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mathieu Sinn | 1 | 55 | 10.41 |
| Bei Chen | 2 | 26 | 9.11 |