论文标题
六：与扭曲的目标平滑推断
SIXO: Smoothing Inference with Twisted Objectives
论文作者
论文摘要
顺序蒙特卡洛（SMC）是一种用于状态空间模型的推理算法，该算法是通过从一系列目标分布的序列中取样近似后验的。目标分布通常被选择为过滤分布，但是这些忽略了未来观察的信息，从而导致推理和模型学习的实际和理论局限性。我们介绍了Sixo，一种方法是学习近似平滑分布的目标，并结合了所有观测值的信息。关键思想是使用密度比估计来拟合将过滤分布扭曲到平滑分布中的功能。然后，我们将SMC与这些学习的目标一起使用，以定义模型和建议学习的变分目标。六体的产量可证明更紧密的对数边缘下限，并在多种域中提供了更准确的后验推断和参数估计。
Sequential Monte Carlo (SMC) is an inference algorithm for state space models that approximates the posterior by sampling from a sequence of target distributions. The target distributions are often chosen to be the filtering distributions, but these ignore information from future observations, leading to practical and theoretical limitations in inference and model learning. We introduce SIXO, a method that instead learns targets that approximate the smoothing distributions, incorporating information from all observations. The key idea is to use density ratio estimation to fit functions that warp the filtering distributions into the smoothing distributions. We then use SMC with these learned targets to define a variational objective for model and proposal learning. SIXO yields provably tighter log marginal lower bounds and offers significantly more accurate posterior inferences and parameter estimates in a variety of domains.
