We present a new transport-based approach to efficiently perform sequential Bayesian inference of static model parameters. The strategy is based on the extraction of conditional distribution from the joint distribution of parameters and data, via the estimation of structured (e.g., block triangular) transport maps. This gives explicit surrogate models for the likelihood functions and their gradients. This allow gradient-based characterizations of posterior density via transport maps in a model-free, online phase. This framework is well suited for parameter estimation in case of complex noise models including nuisance parameters and when the forward model is only known as a black box. The numerical application of this method is performed in the context of characterization of ice thickness with conductivity measurements.

翻译：我们提出了一种基于传输的新方法，用于高效执行静态模型参数的序列贝叶斯推断。该策略通过估计结构化（例如块三角）传输映射，从参数与数据的联合分布中提取条件分布，从而得到似然函数及其梯度的显式代理模型。这允许在无模型在线阶段通过传输映射对后验密度进行基于梯度的表征。该框架特别适用于包含干扰参数的复杂噪声模型参数估计，以及当正向模型仅作为黑箱已知的情况。本方法的数值应用在基于电导率测量的冰层厚度表征场景中得到了实现。