Parameter estimation system, parameter estimation method, and parameter estimation program recording medium for estimating parameter and kernel functions by incorporating machine learning
Abstract:
A first sample acquisition unit acquires a parameter sample from a prior distribution. A function execution unit acquires data from a distribution by supplying the sample to a function. A degree-of-similarity calculation unit calculates the degree of similarity between the data and correct data using a kernel function. A kernel mean construction unit constructs a kernel mean of a posterior distribution from the degree of similarity, the sample, and the kernel function. A second sample acquisition unit acquires a new parameter sample from the kernel mean and the kernel function. A sample evaluation unit determines whether the difference between new data obtained by supplying one sample selected from the new samples to the function and the correct data is less than a prescribed threshold value. When it is determined that the difference is less than the prescribed threshold value, the sample evaluation unit estimates the selected sample as a parameter. The present invention enables estimation of a high-dimensional parameter of the function, thus making it possible to reduce calculation time.
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