Abstract: An information processing device acquires, based on a set including a plurality of samples in which a first value and a second value are associated with each other, a first distribution that is a probability distribution of a first function for calculating the second value from the first value. The information processing device acquires, based on a second distribution having an average different from an average of each argument of the first function in the first distribution, a second function for calculating an evaluation value for an argument value of the first function. The information processing device determines, based on the evaluation value by the second function, an argument value for sampling a function value of the first function.

Abstract: An analysis device applies, for each of a plurality of candidates set according to a update target parameter value, the update target parameter value and the candidate to a machine learning result to acquire information, the information indicating a degree of difference of an evaluation target value in a case of the candidate with respect to an evaluation target value in a case of the update target parameter value; calculates, for each candidate, an evaluation target value in a case of the candidate, based on the degree of difference of the evaluation target values and the evaluation target value in the case of the update target parameter value; and compares the evaluation target values in a case of each of the plurality of candidates and selects a candidate from the plurality of candidates based on a result of the comparison.

Abstract: An analysis device applies, for each of a plurality of candidates for an updated parameter value set according to an update target parameter value, the update target parameter value and the candidate to a plurality of machine learning results to acquire, for each machine learning result, information indicating a degree of difference of an evaluation target value in a case of the candidate with respect to an evaluation target value in a case of the update target parameter value; calculate, for each candidate and for each machine learning result, an evaluation target value in the case of the candidate based on the degree of difference and the evaluation target value in the case of the update target parameter value; and calculate a selection index value for each candidate using a variation in the evaluation target values for each machine learning result, compare the selection index value of each candidate.