Optimal Segmentation of Random Sequences

Abstract. This paper deals with maximum likelihood and least square segmentation of autoregressive random sequences with abruptly changing parameters. Conditional distribution of the observations has been derived. Objective function was modified to the form suitable to apply dynamic programming method for its optimization. Expressions of Bellman functions for this case were obtained. Performance of presented approach is illustrated with simulation examples and segmentation of speech signals examples. Key words: optimal segmentation, maximum likelihood, least square, dynamic programming.

• Type: Journal
• Year: 2000
• Where: INFORMATICALT
• Authors: Antanas Lipeika
• Keywords: Autoregressive Random Sequences, Dynamic Programming, INFORMATICALT 2000, Maximum Likelihood