Bayesian Learning of Logical Hidden Markov Models
Tapani Raiko, Kristian Kersting, Juha Karhunen, Luc De Raedt · 2002
Logical hidden Markov models (LOHMMs) are a generalisation of hidden Markov models to analyze sequences of logical atoms. Transitions are factorized into two steps, selecting an atom and instantiating the variables. Uni cation is used to share information among states, and between states and observations. In this paper, we show how LOHMMs can be learned using Bayesian methods. Some estimators are compared and parameter estimation is tested with synthetic data.