Simulating mixtures of distributions with signed weights proves a challenge as standard simulation algorithms are inefficient in handling the negative weights. In particular, the natural representation of mixture variates as associated with latent component indicators is no longer available. We propose here an exact accept-reject algorithm in the general case of finite signed mixtures that relies on optimaly pairing positive and negative components and designing a stratified sampling scheme on pairs. We analyze the performances of our approach, relative to the inverse cdf approach, since the cdf of the distribution remains available for standard signed mixtures.

翻译：带符号权重的混合分布模拟是一项具有挑战性的任务，因为标准模拟算法难以有效处理负权重。特别是，将混合变量与潜在分量指标相关联的自然表示方法不再适用。本文针对有限带符号混合分布的一般情形，提出了一种精确的接受-拒绝算法。该算法通过最优配对正负分量，并在配对分量上设计分层抽样方案来实现。由于对于标准带符号混合分布，其累积分布函数仍然可计算，我们还将所提方法与逆累积分布函数方法进行了性能对比分析。