A technique for stable and fast computation of a variance representing a confidence interval for an estimation result in an estimation apparatus using a neural network including an integrated layer that combines a dropout layer for dropping out part of input data and an FC layer for computing a weight is provided. When input data having a multivariate distribution is supplied to the integrated layer, a data analysis unit 30 determines, based on a numerical distribution of terms formed by respective products of each vector element of the input data and the weight, a data type of each vector element of output data from the integrated layer. An estimated confidence interval computation unit 20 applies an approximate computation method associated with the data type, to analytically compute a variance of each vector element of the output data from the integrated layer based on the input data to the integrated layer.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An information estimation apparatus for performing an estimation process using a neural network including an integrated layer that combines a dropout layer for dropping out a part of input data and an FC layer for computing a weight, the information estimation apparatus comprising: a data analysis unit configured to determine, based on a numerical distribution of terms formed by respective products of each vector element of input data to the integrated layer having a multivariate distribution and the weight, a data type of each vector element of output data from the integrated layer having a multivariate distribution; and an estimated confidence interval computation unit configured to apply an approximate computation method associated with the data type determined by the data analysis unit to computation in the integrated layer, to analytically compute a variance of each vector element of the output data from the integrated layer based on the input data to the integrated layer. 2. The information estimation apparatus according to claim 1, wherein the data analysis unit is configured to determine the data type, based on a result of determining whether or not the terms formed by the respective products of each vector element of the input data to the integrated layer and the weight include a peak term that is exceptionally larger than other terms. 3. The information estimation apparatus according to claim 2, wherein the data analysis unit is configured to set, as a threshold, a value obtained by multiplying, by a predetermined number, a standard deviation of all of the terms formed by the respective products of each vector element of the input data to the integrated layer and the weight, and determine a term larger than the threshold as the peak term. 4. The information estimation apparatus according to claim 2, wherein in the case where the data analysis unit determines the data type in which the peak term is not included, the estimated confidence interval computation unit is configured to compute a distribution of each vector element of the output data from the integrated layer, using, as a sample sum, a sum of all of the terms formed by the respective products of each vector element of the input data to the integrated layer and the weight. 5. The information estimation apparatus according to claim 2, wherein in the case where the peak term that is exceptionally larger than the other terms is included, the data analysis unit is configured to extract at most a predetermined number of peak terms, and determine the data type based on a result of further determining whether or not remaining terms not extracted are small enough to be regarded as zero. 6. The information estimation apparatus according to claim 5, wherein in the case where the data analysis unit determines the data type in which the peak term is included and the remaining terms not extracted are small enough to be regarded as zero, the estimated confidence interval computation unit is configured to compute a distribution of each vector element of the output data from the integrated layer using only the extracted peak terms. 7. The information estimation apparatus according to claim 5, wherein in the case where the data analysis unit determines the data type in which the peak term is included and the remaining terms not extracted are not small enough to be regarded as zero, the estimated confidence interval computation unit is configured to compute a first distribution using only the extracted peak terms, compute a second distribution using a sum of the remaining terms other than the peak terms as a sample sum, and combine the first distribution and the second distribution to compute a distribution of each vector element of the output data from the integrated layer. 8. The information estimation apparatus according to claim 1, wherein in the case where multivariate mixture distributions included in the output data from the integrated layer include individual distributions similar to each other, the estimated confidence interval computation unit is configured to merge and process the similar distributions as one distribution. 9. An information estimation method for performing an estimation process using a neural network including an integrated layer that combines a dropout layer for dropping out a part of input data and an FC layer for computing a weight, the information estimation method comprising: a data analysis step of determining, based on a numerical distribution of terms formed by respective products of each vector element of input data to the integrated layer having a multivariate distribution and the weight, a data type of each vector element of output data from the integrated layer having a multivariate distribution; and an estimated confidence interval computation step of applying an approximate computation method associated with the data type determined in the data analysis step to computation in the integrated layer, to analytically compute a variance of each vector element of the output data from the integrated layer based on the input data to the integrated layer. 10. The information estimation method according to claim 9, wherein the data analysis step includes determining the data type, based on a result of determining whether or not the terms formed by the respective products of each vector element of the input data to the integrated layer and the weight include a peak term that is exceptionally larger than other terms. 11. The information estimation method according to claim 10, wherein the data analysis step includes setting, as a threshold, a value obtained by multiplying, by a predetermined number, a standard deviation of all of the terms formed by the respective products of each vector element of the input data to the integrated layer and the weight, and determining a term larger than the threshold as the peak term. 12. The information estimation method according to claim 10, wherein in the case of determining the data type in which the peak term is not included, the estimated confidence interval computation step includes computing a distribution of each vector element of the output data from the integrated layer, using, as a sample sum, a sum of all of the terms formed by the respective products of each vector element of the input data to the integrated layer and the weight. 13. The information estimation method according to claim 10, wherein in the case where the peak term that is exceptionally larger than the other terms is included, the data analysis step includes extracting at most a predetermined number of peak terms, and determining the data type based on a result of further determining whether or not remaining terms not extracted are small enough to be regarded as zero. 14. The information estimation method according to claim 13, wherein in the case of determining the data type in which the peak term is included and the remaining terms not extracted are small enough to be regarded as zero, the estimated confidence interval computation step includes computing a distribution of each vector element of the output data from the integrated layer using only the extracted peak terms. 15. The information estimation method according to claim 13, wherein in the case of determining the data type in which the peak term is included and the remaining terms not extracted are not small enough to be regarded as zero, the estimated confidence interval computation step includes computing a first distribution using only the extracted peak terms, computing a second distribution using a sum of the remaining terms other than the peak terms as a sample sum, and combining the first distribution and the second distribution to compute a distribution of each vector element of the output data from the integrated layer. 16. The information estimation method according to claim 9, wherein in the case where multivariate mixture distributions included in the output data from the integrated layer include individual distributions similar to each other, the estimated confidence interval computation step includes merging and processing the similar distributions as one distribution.