2012
NeurIPS
NeurIPS 2012
Mixing Properties of Conditional Markov Chains with Unbounded Feature Functions
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.
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Interdisciplinary Bridge
- Artificial Intelligence and Mathematics & Optimization
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Trend Setter
- Sequence Modeling
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Hot Topic Early Bird
- conditional random field
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Optimization & Theory > Statistical Learning
Machine Learning > Optimization & Theory > Theory
Mathematics & Optimization > Mathematics > Probability
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Core Methods > Graphical Models
Machine Learning > Learning Types > Supervised Learning
Mathematics & Optimization > Probability > Stochastic Processes
Machine Learning > Core Methods > Sequence Modeling
Machine Learning > Bayesian & Probabilistic > Conditional Random Fields