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：模拟带符号权重的分布混合是一个挑战，因为标准模拟算法在处理负权重时效率低下。特别是，混合变量与潜在成分指标相关联的自然表示不再适用。本文针对有限带符号混合的一般情形提出一种精确的接受-拒绝算法，该算法通过优化配对正负成分并在配对集合上设计分层抽样方案实现。我们分析了该方法相对于逆累积分布函数方法的性能表现，因为标准带符号混合分布的累积分布函数仍然可解析获得。