| Budget Amount *help |
¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2012: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2011: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2010: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Research Abstract |
The purpose of this project is to establish a general-purpose use of Bayesian learning for hierarchical probabilistic model such as a neural network and a hidden Markov model. We use an exchange Monte Carlo method for performing Bayesian learning efficiently, and significantly improve the computational cost by parallelizing the exhcnage Monte Carlo method. Moreover, we construct a optimal design of the exchange Monte Carlo method and apply the proposed method to the spectral deconvolution for the radial basis function networks.
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