We study the independent approval model (IAM) for approval elections, where each candidate has its own approval probability and is approved independently of the other ones. This model generalizes, e.g., the impartial culture, the Hamming noise model, and the resampling model. We propose algorithms for learning IAMs and their mixtures from data, using either maximum likelihood estimation or Bayesian learning. We then apply these algorithms to a large set of elections from the Pabulib database. In particular, we find that single-component models are rarely sufficient to capture the complexity of real-life data, whereas their mixtures perform well.

翻译：我们研究了认可选举中的独立认可模型（IAM），其中每位候选人具有独立的认可概率，且其认可状态与其他候选人无关。该模型推广了多种经典模型，例如公正文化模型、汉明噪声模型和重采样模型。我们提出了基于最大似然估计或贝叶斯学习的算法，用于从数据中学习IAM及其混合模型。随后，我们将这些算法应用于Pabulib数据库中的大规模选举数据集。研究发现，单组分模型通常难以捕捉真实数据的复杂性，而其混合模型则表现出良好的拟合性能。