Sampler for Composition Ratio by Markov Chain Monte Carlo
This work addresses a specific sampling challenge in creative combination tasks, such as cocktail creation, but appears incremental as it builds on existing MCMC methods with tailored constraints.
The paper tackles the problem of sampling composition ratios, which are nonnegative-integer-valued vectors with a constant sum and few nonzero elements, by proposing a new Markov chain Monte Carlo (MCMC) algorithm that designs a target distribution and restricts value changes to specific pairs per iteration, and demonstrates its application in creating a new cocktail through an experiment combining it with supervised learning.
Invention involves combination, or more precisely, ratios of composition. According to Thomas Edison, "Genius is one percent inspiration and 99 percent perspiration" is an example. In many situations, researchers and inventors already have a variety of data and manage to create something new by using it, but the key problem is how to select and combine knowledge. In this paper, we propose a new Markov chain Monte Carlo (MCMC) algorithm to generate composition ratios, nonnegative-integer-valued vectors with two properties: (i) the sum of the elements of each vector is constant, and (ii) only a small number of elements is nonzero. These constraints make it difficult for existing MCMC algorithms to sample composition ratios. The key points of our approach are (1) designing an appropriate target distribution by using a condition on the number of nonzero elements, and (2) changing values only between a certain pair of elements in each iteration. Through an experiment on creating a new cocktail, we show that the combination of the proposed method with supervised learning can solve a creative problem.