| 摘要 |
To detect a statistical change-point that appears in time-series data with a high accuracy. A first model learning section 102 learns the occurrence probability distribution of time-series data 111 as a first statistical model (for example, a latent Markov model) defined by a finite number of variables including a latent variable. In the subsequent processing, the degree of a temporal change in the probability distribution is computed for each of the probability distribution of the entire first statistical model, its partial probability distribution (the probability distribution of the latent variable and conditional probability distribution contingent on the value of the latent variable), and the probability distribution in which the above plural probability distributions are linearly-combined with a weight. The change-point of the time-series data 111 is detected on the basis of the computed degree of the change.
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