Probabilistic data mining theory using item response theory based on Markov random field

项目反应理论（IRT）是近年来在社会科学和心理学领域发展起来的一种统计检验理论。在传统的IRT模型中，每个项目在统计上是相互独立的。在这个研究项目中，我提出了一个新的IRT概率模型，包括项目之间的相关性，并为所提出的模型提出了近似技术和机器学习算法。由于该模型在数学上等同于神经网络和机器学习领域中众所周知的玻尔兹曼机，因此所提出的方法不仅可以应用于IRT，还可以应用于这些领域。

An item response theory (IRT)is a recent statistical test theory which has been mainly developed in social science and psychology. In conventional models of IRT, each item has been statistically independent of each other. In this research program, I have proposed a new probabilistic model of an IRT including correlations among items, and have proposed approximate techniques and machine learning algorithms for the proposed model. Since the model is mathematically equivalent to Boltzmann machines which are well known in the area of neural networks and the area of machine learning, the proposed methods can be applied to not only the IRT but also applications in those areas.