论文标题
状态空间期望传播：颞高斯过程的有效推理方案
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes
论文作者
论文摘要
我们在非偶联的时间和时空高斯过程模型中制定了近似贝叶斯推断，作为在卡尔曼平滑过程中应用的简单参数更新规则。该观点涵盖了大多数推理方案，包括期望传播（EP），经典（扩展，无知等）Kalman Smoothers和变异推断。我们在这些算法上提供了一个统一的观点，显示了如何用线性化替换功率EP匹配步骤恢复了经典的Smoothers。 EP通过引入所谓的腔分布提供了一些比传统方法的好处，我们将这些好处与线性化的计算效率相结合，提供了广泛的经验分析，证明了在此统一框架下各种算法的功效。我们提供JAX中所有方法的快速实现。
We formulate approximate Bayesian inference in non-conjugate temporal and spatio-temporal Gaussian process models as a simple parameter update rule applied during Kalman smoothing. This viewpoint encompasses most inference schemes, including expectation propagation (EP), the classical (Extended, Unscented, etc.) Kalman smoothers, and variational inference. We provide a unifying perspective on these algorithms, showing how replacing the power EP moment matching step with linearisation recovers the classical smoothers. EP provides some benefits over the traditional methods via introduction of the so-called cavity distribution, and we combine these benefits with the computational efficiency of linearisation, providing extensive empirical analysis demonstrating the efficacy of various algorithms under this unifying framework. We provide a fast implementation of all methods in JAX.
