Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present an efficient method for statistical parameter learning of a certain class of symbolic-statistical models (called PRISM programs) including hidden Markov models (HMMs). To learn the parameters,  adopt the EM algorithm, an iterative method for maximum likelihood estimation. For the efficient parameter learning,  first introduce a specialized data structure for explanations for each observation, and then apply a graph-based EM algorithm. The algorithm can be seen as a generalization of Baum-lch algorithm, an EM algorithm specialized for HMMs.  show that, given appropriate data structure, Baum-lch algorithm can be simulated by our graph-based EM algorithm.
['Yoshitaka Kameya', 'Nobuhisa Ueda', 'Taisuke Sato']
I am writing a research paper on a idea A Graphical Method for Parameter Learning of Symbolic-Statistical Models
[{"from": "human", "value": "I am writing a research paper on a idea A Graphical Method for Parameter Learning of Symbolic-Statistical Models"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present an efficient method for statistical parameter learning of a certain class of symbolic-statistical models (called PRISM programs) including hidden Markov models (HMMs). To learn the parameters,  adopt the EM algorithm, an iterative method for maximum likelihood estimation. For the efficient parameter learning,  first introduce a specialized data structure for explanations for each observation, and then apply a graph-based EM algorithm. The algorithm can be seen as a generalization of Baum-lch algorithm, an EM algorithm specialized for HMMs.  show that, given appropriate data structure, Baum-lch algorithm can be simulated by our graph-based EM algorithm."}]