For a class of partially observed diffusions, conditions are given for the map from the initial condition of the signal to filtering distribution to be contractive with respect to Wasserstein distances, with rate which does not necessarily depend on the dimension of the state-space. The main assumptions are that the signal has affine drift and constant diffusion coefficient and that the likelihood functions are log-concave. Ergodic and nonergodic signals are handled in a single framework. Examples include linear-Gaussian, stochastic volatility, neural spike-train and dynamic generalized linear models. For these examples filter stability can be established without any assumptions on the observations.

对于一类部分观察到的扩散，给出了从信号的初始状态到滤波分布的映射条件，这些条件相对于Wasserstein距离是收缩的，其速率不必取决于状态空间的维数。主要假设是信号具有仿射漂移和恒定扩散系数，似然函数是对数凹形的。遍历和非遍历信号在单个框架中处理。例子包括线性高斯，随机波动率，神经峰值训练和动态广义线性模型。对于这些示例，无需对观测值进行任何假设就可以建立滤波器的稳定性。